Electronic device, and method by which electronic device detects forged fingerprint

Abstract

This electronic device comprises: a display; a touch sensor arranged to overlap the display; a fingerprint sensor arranged to overlap the display; a memory for storing instructions; and at least one processor, wherein the instructions can instruct the electronic device to: acquire, on the basis of a first touch input through the display, a first touch signal sensed through the touch sensor and a first fingerprint image sensed through the fingerprint sensor; and determine whether the first fingerprint image is forged according to a first value of the first touch signal, to whether the electronic device is gripped by the human body, and to whether the electronic device is electrically connected to an external electronic device.

Description

Electronic devices and methods for detecting fake fingerprints in electronic devices

[0001] Embodiments of the present disclosure relate to electronic devices and methods for detecting fake fingerprints in electronic devices.

[0002] Electronic devices can be equipped with various biometric authentication technologies. Among these, fingerprint authentication may be the most commonly used. The electronic device may include a display (e.g., a touchscreen) capable of detecting touches from a finger or other object. A fingerprint sensor positioned at a location corresponding to at least a portion of the electronic device display can be used to acquire a fingerprint.

[0003] Electronic devices can perform various functions using fingerprint authentication. For example, electronic devices can use fingerprint authentication to perform screen lock functions, application lock functions, and / or payment functions.

[0004] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above is applicable as prior art related to the present disclosure.

[0005] Fingerprint authentication attempts can occur using fake fingerprints that are falsified versions of a user's actual fingerprint. When a real finger is touched on an electronic device for fingerprint input, the change in capacitance of the touch sensor may be smaller than the change in capacitance of the touch sensor when the fake fingerprint is touched with a dielectric material due to the thickness and / or permittivity of the dielectric material for the fake fingerprint. The electronic device can detect whether a fingerprint is real or fake based on whether the change in capacitance of the touch sensor is large or small when a touch input is made for fingerprint input.

[0006] If the electronic device is a low ground mass (LGM) device without a separate ground connection, the change in capacitance of the touch sensor may vary depending on whether the electronic device is grounded (e.g., the electronic device is gripped by a human body or an external electronic device is electrically connected to the electronic device). The change in capacitance of the touch sensor due to a touch input when the electronic device is not gripped by a human body and when an external electronic device is not electrically connected to the electronic device may be smaller than when the electronic device is gripped by a human body or an external electronic device is electrically connected to the electronic device.

[0007] When an electronic device is gripped by a human body or an external electronic device is electrically connected to the electronic device and a real finger touches it, the change value of the capacitance of the touch sensor may not be difficult to distinguish from the change value of the capacitance of the touch sensor when touching a dielectric for a fake fingerprint. When an electronic device is not gripped by a human body or an external electronic device is electrically connected to the electronic device and a real finger touches it, the change value of the capacitance of the touch sensor may be small in difference from the change value of the capacitance of the touch sensor when touching a dielectric for a fake fingerprint while the electronic device is gripped by a human body or an external electronic device is electrically connected to the electronic device, making it difficult to accurately detect a fake fingerprint.

[0008] An electronic device according to one embodiment of the present disclosure may include a display, a touch sensor arranged to overlap the display, a fingerprint sensor arranged to overlap the display, a memory for storing commands, and at least one processor. The commands according to one embodiment, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain a first touch signal detected through the touch sensor and a first fingerprint image detected through the fingerprint sensor based on a first touch input through the display. The commands, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device. The above commands, when individually or collectively executed by the at least one processor, may cause the electronic device to determine that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a first threshold value, and to determine that the first fingerprint image is counterfeit if the value of the first touch signal is less than or equal to the first threshold value, when the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device.The above instructions, when executed individually or collectively by the at least one processor, may cause the electronic device to determine that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, and to determine that the first fingerprint image is counterfeit if the first value of the first touch signal is less than or equal to the second threshold value, when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device.

[0009] In one embodiment of the present disclosure, a method for detecting a fake fingerprint in an electronic device may include an operation of acquiring a first touch signal detected by a touch sensor and a first fingerprint image detected by a fingerprint sensor based on a first touch input through a display of the electronic device. The method may include an operation of identifying a first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device. The method may include an operation of determining that the first fingerprint image is not fake if the first value of the first touch signal exceeds a first threshold value, and determining that the first fingerprint image is fake if the value of the first touch signal is less than or equal to the first threshold value, when the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device. The method may include an operation of determining that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, and determining that the first fingerprint image is counterfeit if the first value of the first touch signal is less than or equal to the second threshold value, when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device.

[0010] According to one embodiment, a non-transitory storage medium storing commands is provided, wherein the commands, when executed by an electronic device, are configured to cause the electronic device to perform at least one operation, wherein the at least one operation may include an operation of acquiring a first touch signal detected through a touch sensor (271) and a first fingerprint image detected through a fingerprint sensor (273) based on a first touch input through a display (160, 260) of the electronic device. The at least one operation may include an operation of identifying a first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device. The at least one operation may include, when the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device, an operation of determining that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a first threshold value, and an operation of determining that the first fingerprint image is counterfeit if the value of the first touch signal is less than or equal to the first threshold value, when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device, an operation of determining that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, and an operation of determining that the first fingerprint image is counterfeit if the first value of the first touch signal is less than or equal to the second threshold value.

[0011] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0012] Figure 2 is a block diagram of an electronic device according to one embodiment.

[0013] FIG. 3 is a diagram showing examples of values ​​of touch signals by a dielectric including a fake fingerprint according to one embodiment.

[0014] FIG. 4a is a diagram showing vertical TX lines and horizontal RX lines of a touch sensor according to one embodiment.

[0015] FIG. 4b is a drawing for explaining a change in electrostatic capacitance formed between a touch sensor and a finger according to one embodiment.

[0016] FIG. 5 is a diagram showing an example of obtaining a value of electrostatic capacitance between a TX line and an RX line according to one embodiment.

[0017] FIG. 6 is a diagram showing touch maps for touch input corresponding to a real fingerprint and touch input corresponding to a fake fingerprint under the same conditions according to one embodiment.

[0018] FIG. 7 is a diagram showing touch maps corresponding to actual fingerprints when a touch input is performed on an electronic device in a state where the electronic device is gripped by a human body and in a state where the electronic device is not gripped by a human body according to one embodiment.

[0019] FIG. 8 is a diagram illustrating touch maps corresponding to actual fingerprints when a touch input is performed in a state in which an electronic device is electrically connected to an external electronic device and in a state in which the electronic device is not electrically connected to an external electronic device according to one embodiment.

[0020] FIG. 9 is a diagram showing three-dimensional touch maps according to the type of fake fingerprint according to one embodiment.

[0021] FIG. 10 is a diagram showing graphs of changes in the maximum value on the touch map over time according to the type of fake fingerprint according to one embodiment.

[0022] FIG. 11 is a diagram for explaining an example of determining a first threshold value and a second threshold value according to one embodiment.

[0023] FIG. 12a is a histogram showing the distribution of Yaw values ​​of a gyro sensor depending on whether or not the electronic device is gripped by a human body according to one embodiment.

[0024] FIG. 12b is a histogram showing the distribution of pitch values ​​of a gyro sensor depending on whether or not the electronic device is gripped by a human body according to one embodiment.

[0025] FIG. 12c is a histogram showing the distribution of the Roll value of the gyro sensor depending on whether the electronic device is gripped by a human body according to one embodiment.

[0026] FIG. 13a is a histogram showing the distribution of x-angle values ​​of an acceleration sensor depending on whether or not the electronic device is gripped by a human body according to one embodiment.

[0027] FIG. 13b is a histogram showing the distribution of y-angle values ​​of an acceleration sensor depending on whether or not the electronic device is gripped by a human body according to one embodiment.

[0028] FIG. 13c is a histogram showing the distribution of z-angle values ​​of an acceleration sensor depending on whether or not the electronic device is gripped by a human body according to one embodiment.

[0029] FIG. 14a is a histogram showing the distribution of differences in Yaw values ​​of a gyro sensor at a specified time interval depending on whether the electronic device is gripped by a human body according to an embodiment.

[0030] FIG. 14b is a histogram showing the distribution of differences in pitch values ​​of a gyro sensor at a specified time interval depending on whether or not the electronic device is gripped by a human body according to an embodiment.

[0031] FIG. 14c is a histogram showing the distribution of differences in Roll values ​​of a gyro sensor at a specified time interval depending on whether the electronic device is gripped by a human body according to an embodiment.

[0032] FIG. 15a is a histogram showing the distribution of differences in x-angle values ​​at a specified time interval of an acceleration sensor depending on whether or not the electronic device is gripped by a human body according to one embodiment.

[0033] FIG. 15b is a histogram showing the distribution of differences in y-angle values ​​of a specified time interval of an acceleration sensor depending on whether or not the electronic device is gripped by a human body according to one embodiment.

[0034] FIG. 15c is a histogram showing the distribution of differences in z-angle values ​​of a gyro sensor at a specified time interval depending on whether the electronic device is gripped by a human body according to an embodiment.

[0035] FIG. 16 is a flowchart illustrating a fake fingerprint detection operation in an electronic device according to one embodiment.

[0036] FIG. 17a is a flowchart illustrating a fake fingerprint detection operation according to the validity of gyro sensor or acceleration sensor data when a fingerprint is input in an electronic device according to one embodiment.

[0037] Figure 17b is a flowchart showing the operation continuing from Figure 17a.

[0038] In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0039] The terms used in this document are used only to describe specific embodiments and may not be intended to limit the scope of the embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise. All terms used herein, including technical or scientific terms, have the same meaning as commonly understood by those of ordinary skill in the art of this disclosure. Terms defined in commonly used dictionaries may be interpreted as having the same or similar meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this document. In some cases, even if a term is defined in this document, it cannot be interpreted to exclude embodiments of this document.

[0040] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to one embodiment.

[0041] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0042] The processor (120) may control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) by executing, for example, software (e.g., a program (140)), and may perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculation, the processor (120) may store a command or data received from another component (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the command or data stored in the volatile memory (132), and store the resulting data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0043] The auxiliary processor (123) may control at least a part of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0044] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0045] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0046] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0047] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0048] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0049] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0050] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0051] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0052] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0053] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0054] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0055] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0056] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0057] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0058] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) may support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0059] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the selected at least one antenna. According to some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0060] In one embodiment, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent to a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0061] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0062] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service on its own, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In one embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0063] Figure 2 is a block diagram of an electronic device according to one embodiment.

[0064] Referring to FIG. 2, an electronic device (201) according to an embodiment (e.g., the electronic device (101) of FIG. 1) may include a processor (220), a memory (230), a display (260), a touch sensor (271), and a fingerprint sensor (273). The electronic device (201) according to an embodiment may further include a gyro sensor (275), an acceleration sensor (277), and / or a connector (278). The electronic device (201) according to an embodiment may further include a grip sensor (not shown). The electronic device (201) according to an embodiment may also include a motion sensor including a gyro sensor (275) and an acceleration sensor (277). The electronic device (201) according to an embodiment is not limited thereto and may further include all or part of the electronic device (101) illustrated in FIG. 1.

[0065] A processor (220) according to an embodiment (e.g., processor (120) of FIG. 1) may include a circuit for processing and may include some or all of a central processing unit (CPU), an application processor (AP), a neural processing unit (NPU), and a graphics processing unit (GPU). The processor (220) according to an embodiment may mean at least one processor. The processor (220) according to an embodiment may perform an overall control operation of the electronic device (201) and may perform at least one operation for performing the method for detecting a fake fingerprint of the present disclosure.

[0066] A memory (230) according to one embodiment (e.g., memory (130) of FIG. 1) may include one or more storage media that store commands (e.g., instructions) that are individually or collectively executed by at least one processor (220). The memory (230) according to one embodiment may store various data used for operations associated with at least one component (e.g., processor (220), display (260), touch sensor (271), fingerprint sensor (273), gyro sensor (275), acceleration sensor (277), and / or connector (278) of the electronic device (201). The data may include, for example, software (e.g., software module or program (140)) and input data or output data for commands related thereto. The memory (230) according to one embodiment may store various data generated during execution of a program, including a program (e.g., software or program (140) of FIG. 1) for performing fake fingerprint detection. The memory (230) according to one embodiment may store commands (or instructions) that, when executed by the processor (220), cause the electronic device (201) to perform the fake fingerprint detection operation (or method) of the present disclosure. According to one embodiment The memory (230) can store a registered fingerprint image (or fingerprint information or fingerprint template). According to one embodiment, the memory (230) can include commands (or instructions) (e.g., a program (or software or algorithm)) that, when executed by the processor (220), cause the electronic device (201) to perform a fingerprint recognition operation (or method) using the registered fingerprint image.According to one embodiment, the memory (230) may store a first threshold value for determining whether a fingerprint image is forged when the electronic device (201) is gripped by a human body or connected to an external electronic device, and a second threshold value for determining whether a fingerprint image is forged when the electronic device (201) is not gripped by a human body or connected to an external electronic device.

[0067] A display (260) according to an embodiment (e.g., display (160) of FIG. 1) may include a cover window (not shown) and a display panel (not shown) disposed below the cover window. The display (260) according to an embodiment may display various information through the display panel. For example, the display (260) may display information indicating a location in the display area where a fingerprint input function was performed. When a fake fingerprint is detected (or determined), the display (260) may display information indicating that the fingerprint is fake. When user authentication using a fingerprint input (e.g., a fake fingerprint) fails, the display (260) may display information indicating that the user authentication has failed.

[0068] A touch sensor (271) according to an embodiment may be arranged to overlap with a display (260). A touch sensor (271) according to an embodiment may be arranged between a cover window of the display (260) and a display panel. A touch sensor (271) according to an embodiment may also be included in the display (260). A touch sensor (271) according to an embodiment may sense a touch signal based on a user's touch (or touch input) through the display (260). A touch sensor (271) according to an embodiment may include a touch screen panel using an electrostatic capacitance method. A touch screen panel according to one embodiment includes TX lines in a vertical direction (e.g., x direction) and RX lines in a horizontal direction (e.g., Y direction), and can form mutual capacitance (or self-capacitance) through the TX lines and the RX lines in the horizontal direction (e.g., Y direction) and sense (or detect) a touch signal (or a change in intensity of a touch signal) (e.g., a change in capacitance).

[0069] A fingerprint sensor (273) (e.g., a fingerprint recognition sensor) according to an embodiment may be arranged to overlap with the display (260) and the touch sensor (271). The fingerprint sensor (273) according to an embodiment may be arranged on the top of the touch sensor (271) between the cover window of the display (260) and the display panel. The fingerprint sensor (273) according to an embodiment may sense (or detect) (or acquire) a fingerprint image (or fingerprint information) based on a user's touch (or touch input) through the display (260). The fingerprint sensor (273) according to an embodiment may sense a fingerprint image using an optical method based on the difference in light reflected by ridges and valleys constituting a fingerprint, an ultrasonic method based on the phase difference of ultrasonic waves reflected by ridges and valleys, or an electrostatic method based on the difference in permittivity caused by ridges and valleys. The method by which the fingerprint sensor (273) acquires a fingerprint image is not limited to the examples described above.

[0070] A gyro sensor (275) according to an embodiment can sense (or detect or obtain) a rotational direction or rotational angle of an electronic device (201). A gyro sensor (275) according to an embodiment can sense (or detect or obtain) a plurality of angular velocity values ​​(e.g., a yaw-axis angular velocity value, a pitch-axis angular velocity value, a roll-axis angular velocity value) corresponding to a plurality of axes (e.g., a yaw-axis, a pitch-axis, a roll-axis).

[0071] An acceleration sensor (277) according to an embodiment can sense (or measure or detect) acceleration values ​​due to movement of an electronic device (201). An acceleration sensor (277) according to an embodiment can sense (or detect or obtain) a plurality of acceleration values ​​(e.g., an x-axis acceleration value, a y-axis acceleration value, a z-axis acceleration value) corresponding to a plurality of axes (e.g., an x-axis, a y-axis, a z-axis).

[0072] A connector (278) according to one embodiment (e.g., a connection terminal (178) of FIG. 1) may include a member that allows the electronic device (201) to be physically (and electrically) connected to an external device. According to one embodiment, a cable may be connected to the connector (278), and the electronic device (201) and the external device (e.g., the electronic device (102)) may be electrically connected through the cable. For example, the connector (278) may include an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0073] According to one embodiment, a processor (220) may obtain a first touch signal (e.g., a signal corresponding to a first electrostatic capacitance change value) corresponding to the first touch input through a touch sensor (271) based on a touch input (hereinafter, also referred to as a “first touch input”) through a display (260) (e.g., when a touch input is made to obtain a user’s fingerprint image), and may obtain a first fingerprint image corresponding to the first touch input through a fingerprint sensor (273).

[0074] According to one embodiment, the processor (220) can identify the value of the first touch signal, whether the electronic device (201) is gripped by a human body (e.g., a user's hand), and whether the electronic device (201) is electrically connected to an external device based on the acquisition of the first touch signal and the first fingerprint image.

[0075] According to an embodiment, the processor (220) may obtain a plurality of values ​​on the touch map corresponding to the first touch signal by using the touch map of the touch sensor (271). The touch map according to an embodiment may be a map indicating x, y coordinates mapped so that each location (x, y coordinates) of the display (260) (e.g., display panel) and each location (x, y coordinates) of the touch sensor (271) (e.g., touch screen panel) match. According to an embodiment, the processor (220) may obtain a first touch signal sensed by the touch sensor (271) according to a first touch input in which a part of the entire area of ​​the display (260) is touched by the user. Based on the acquisition of the first touch signal, the processor (220) according to an embodiment may identify a plurality of coordinate values ​​of the touched area by using the touch map and obtain a plurality of values ​​(e.g., a plurality of touch signal intensity values) corresponding to each coordinate value. According to one embodiment, the processor (220) may determine the maximum value among a plurality of touch signal intensity values ​​as the value of the first touch signal, or may determine the sum of some touch signal intensity values ​​having a value greater than the remaining portions among the plurality of touch signal intensity values ​​as the first value of the first touch signal.

[0076] According to one embodiment, a processor (220) can identify whether an electronic device (201) is gripped by a human body (e.g., a user's hand) using values ​​(e.g., sensor data) sensed by a gyro sensor (275) and / or an acceleration sensor (277).

[0077] According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body if the distribution range of values ​​sensed by the gyro sensor (275) (e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) is narrower than a specified first distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of values ​​sensed by the gyro sensor (275) is equal to or wider than the first distribution range. According to an embodiment, if the electronic device (201) is not gripped by a human body (e.g., a user's hand), the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) may be distributed in a narrow distribution range close to 0. According to one embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) may be distributed in a wide distribution range. According to one embodiment, the first distribution range may be determined as a distribution range that can distinguish between a narrow distribution of the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0078] According to an embodiment, the processor (220) may obtain first sensed values ​​(e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) by the gyro sensor (275), obtain second sensed values ​​(e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) by the gyro sensor (275) after a specified time, and detect differences (or changes) between the first sensed values ​​and the second sensed values. According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body when a distribution range of differences (or changes) between the first sensed values ​​and the second sensed values ​​is narrower than a specified second distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the differences (or changes) between the first sensed values ​​and the second sensed values ​​is equal to or wider than the second distribution range. According to an embodiment, when the electronic device (201) is not gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a narrow distribution range close to 0. According to an embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a wide distribution range. According to one embodiment, the second distribution range may be determined as a distribution range that can distinguish between a narrow distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0079] According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body if the distribution range of the values ​​(e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) sensed by the acceleration sensor (277) is narrower than a designated third distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the values ​​sensed by the acceleration sensor (277) is equal to or wider than the third distribution range. According to an embodiment, when the electronic device (201) is not gripped by a human body (e.g., a user's hand), the x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values ​​sensed by the acceleration sensor (277) may be distributed in a narrow distribution range close to 0. According to one embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values ​​sensed by the acceleration sensor (277) may be distributed in a wide distribution range. According to one embodiment, the third distribution range may be determined as a distribution range that can distinguish between a narrow distribution of the x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values ​​sensed by the acceleration sensor (277) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of the x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values ​​sensed by the acceleration sensor (277) when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0080] According to an embodiment, the processor (220) may obtain first sensed values ​​(e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) by the acceleration sensor (277), obtain second sensed values ​​(e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) by the acceleration sensor (277) after a specified time, and detect differences (or changes) between the first sensed values ​​and the second sensed values. According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body when a distribution range of differences (or changes) between the first sensed values ​​and the second sensed values ​​is narrower than a specified fourth distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the differences (or changes) between the first sensed values ​​and the second sensed values ​​is equal to or wider than the fourth distribution range. According to an embodiment, when the electronic device (201) is not gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a narrow distribution range close to 0. According to an embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a wide distribution range. According to one embodiment, the fourth distribution range may be determined as a distribution range that can distinguish between a narrow distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0081] According to one embodiment, the processor (220) may determine whether the electronic device (201) is gripped by a human body by using some or all of the following: a distribution range of values ​​sensed by the gyro sensor (275) (e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values), a distribution range of differences (or changes) between first sensed values ​​and second sensed values ​​by the gyro sensor (275), a distribution range of values ​​sensed by the acceleration sensor (277) (e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values), and a distribution range of differences (or changes) between first sensed values ​​and second sensed values ​​by the acceleration sensor (277). According to one embodiment, the processor (220) may use a grip sensor instead of a gyro sensor (275) and an acceleration sensor (277) to determine whether the electronic device (201) is gripped by a human body.

[0082] In one embodiment, the processor (220) may identify that the electronic device (201) is electrically connected to an external device (e.g., electronic device (102)) when a cable (e.g., a wired cable) (or a USB device or a charging device) is connected to the connector (278). In one embodiment, the processor (220) may identify that the electronic device (201) is not electrically connected to the external device when a cable (or a USB device or a charging device) is not connected to the connector (278).

[0083] In one embodiment, the processor (220) may determine that the first fingerprint image is not counterfeit if the value (e.g., the first value) of the first touch signal exceeds a first threshold when the electronic device (201) is in at least one of a state in which the electronic device is gripped by a human body (e.g., a first state) or a state in which the electronic device is electrically connected to the external electronic device (e.g., a second state), and may determine that the first fingerprint image is counterfeit if the first value of the first touch signal is less than or equal to the first threshold. In one embodiment, the processor (220) may determine that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a second threshold that is less than the first threshold when the electronic device (201) is not gripped by a human body and is not electrically connected to an external electronic device, and may determine that the first fingerprint image is not counterfeit if the first value of the first touch signal is less than or equal to the second threshold. According to one embodiment, if the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are invalid, the result of identifying whether the electronic device (201) is gripped by a human body (e.g., a user's hand) by the processor (220) may be erroneous. According to one embodiment, the processor (220) may determine whether the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are included in a specified valid value range, and if the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are included in the valid value range, the processor may perform an operation of determining whether the fingerprint image is forged using a first threshold value and a second threshold value. According to one embodiment, the processor (220) may determine whether a fingerprint image is forged by using another forged fingerprint detection method (e.g., an image learning-based forged fingerprint detection model) without performing a forged determination operation using the first threshold value and the second threshold value when the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are not included in the valid value range.According to an embodiment, the processor (220) may utilize an artificial intelligence neural network based on a generative AI model as another fake fingerprint detection method. For example, the generative AI model may include an image generation model and / or a language generation model. The image generation model may include a generative adversarial network (GAN) and / or a variational autoencoder (VAE). An example of an image generation model may include a diffusion-based generative model using the structure of a VAE and a transformer. In addition, the processor (220) may include large multimodal models (LMMs) capable of recognizing various types of data input, such as text, images, and voice, and generating new data corresponding thereto. According to an embodiment, the generative AI model may be an anti-spoofing-protection (ASP) artificial intelligence model that distinguishes between fake and non-fake fingerprint images. The ASP artificial intelligence model can determine whether a fingerprint image input through a fingerprint sensor (273) is a forged fingerprint.

[0084] In one embodiment, a memory (230) may store a first classifier (classifier1) trained to determine whether a fingerprint image is forged based on whether a value of a touch signal exceeds or is below a first threshold value, and a second classifier (classifier2) trained to determine whether a fingerprint image is forged based on whether a value of a touch signal exceeds or is below a second threshold value. In one embodiment, when a genome including a forged fingerprint is touched depending on the type of the forged fingerprint, the 2D / 3D footprint distribution shape of the touch map may appear differently. According to an embodiment, the first classifier and the second classifier may be classifiers trained by manually setting parameters based on features of values ​​of a 2D or 3D touch map corresponding to each of a plurality of types of fake fingerprints (e.g., shape of a graph based on values ​​of a 2D or 3D touch map (whether the center is sharp or flat), slope of a graph based on values ​​of a 2D or 3D touch map (magnitude of a slope at which the value of a touch signal decreases from the center to the periphery of the touch map), or distribution of values ​​of a 2D or 3D touch map (whether the touch signal is evenly distributed or concentrated in a few sizes)), or automatically setting parameters through machine learning, or setting parameters through deep learning.

[0085] In one embodiment, the processor (220) may determine whether a fingerprint is fake by applying a first classifier to the first value of the first touch signal when the first value of the first touch signal is obtained in at least one of a state in which the electronic device (201) is gripped by a human body (e.g., a first state) or a state in which the electronic device (201) is electrically connected to an external electronic device (e.g., a second state). In one embodiment, the processor (220) may determine (or identify) whether a fingerprint is fake by applying a second classifier to the first value of the first touch signal when the first value of the first touch signal is obtained in a state in which the electronic device (201) is not gripped by a human body and is not electrically connected to an external electronic device.

[0086] According to an embodiment, the processor (220) can identify a case where the electronic device (201) is gripped by a human body or electrically connected to an external device and a case where the electronic device (201) is not gripped by a human body and not electrically connected to an external device. According to an embodiment, the processor (220) uses a first threshold value as a reference value for determining whether the value of a touch signal corresponds to a fake fingerprint when the electronic device (201) is gripped by a human body or electrically connected to an external device, and uses a second threshold value smaller than the first threshold value as a reference value for determining whether the value of the touch signal corresponds to a fake fingerprint when the electronic device (201) is not gripped by a human body and not electrically connected to an external device, thereby increasing the accuracy of determining whether or not a fingerprint is fake.

[0087] According to an embodiment, the processor (220) may obtain a plurality of touch signals corresponding to a plurality of touch inputs of a specific user through the touch sensor (271) in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device. According to an embodiment, the processor (220) may determine a first threshold value for a specific user by multiplying an average of at least some values ​​of successful fingerprint authentication among the values ​​of the plurality of touch signals obtained in at least one of the first state in which the electronic device (201) is gripped by a human body or the second state in which the electronic device (201) is electrically connected to an external electronic device by a first relationship constant. In one embodiment, the processor (220) may determine the second threshold for the specific user by multiplying an average of at least some values ​​of successful fingerprint authentication among the values ​​of a plurality of touch signals obtained in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device by a second relationship constant. The first relationship constant and the second relationship constant according to one embodiment may be constant values ​​derived in advance through experiments in at least one of the first state in which the electronic device (201) is gripped by a human body or the second state in which the electronic device (201) is electrically connected to an external electronic device.

[0088] According to an embodiment, a processor (220) may perform multiple fingerprint recognition by receiving multiple touch inputs from a specific user to register fingerprint information, and may acquire N (e.g., 10) touch signals (or touch data) in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device during a fingerprint recognition process for registering fingerprint information. According to an embodiment, the processor (220) may determine a first threshold value for a specific user by multiplying an average of the maximum values ​​of each of the N touch signals acquired during the fingerprint recognition process by a first relationship constant.

[0089] The following mathematical expression 1 may be a mathematical expression that determines the first threshold value.

[0090]

[0091] In the above mathematical expression 1, Thres1 is a first threshold value, k1 is a first relationship constant value, and Avg may be an average of the maximum values ​​of each of the N touch signals.

[0092] According to one embodiment, the processor (220) may determine a second threshold value for a specific user by multiplying the average of the maximum values ​​of each of the N touch signals acquired during the fingerprint recognition process by a second relationship constant.

[0093] The following mathematical expression 2 may be a mathematical expression that determines the second threshold value.

[0094]

[0095] In the above mathematical expression 2, Thres2 is a second threshold value, k2 is a second relationship constant value, and Avg may be an average of the maximum values ​​of each of the N touch signals.

[0096] According to an embodiment, the processor (220) may obtain a plurality of touch signals corresponding to a plurality of touch inputs of a specific user through the touch sensor (271) in a state where the electronic device (201) is not gripped by a human body and is not electrically connected to an external electronic device. According to an embodiment, the processor (220) may determine a first threshold for a specific user by multiplying an average of at least some values ​​for which fingerprint authentication is successful among the values ​​of the plurality of touch signals obtained in a state where the electronic device (201) is not gripped by a human body and is not electrically connected to an external electronic device by a third relationship constant. According to an embodiment, the processor (220) may determine a second threshold for the specific user by multiplying an average of at least some values ​​for which fingerprint authentication is successful among the values ​​of the plurality of touch signals obtained in a state where the electronic device (201) is not gripped by a human body and is not electrically connected to an external electronic device by a fourth relationship constant. The third relation constant and the fourth relation constant according to one embodiment may be constant values ​​derived in advance through an experiment in a state where the electronic device (201) is not gripped by a human body and is not electrically connected to an external electronic device.

[0097] According to one embodiment, the processor (220) may acquire a touch signal (or touch data) every time fingerprint authentication is successful even after the fingerprint recognition process for registering fingerprint information is completed, and may update the first threshold value and the second threshold value using a moving average every time a touch signal is acquired.

[0098] According to one embodiment of the present disclosure, it is possible to determine whether an electronic device is gripped by a human body and / or whether an external electronic device is electrically connected to the electronic device, and to use different threshold values ​​for determining whether a fingerprint is fake depending on whether the electronic device is gripped by a human body or an external electronic device is electrically connected to the electronic device, or whether the electronic device is not gripped by a human body and an external electronic device is not electrically connected to the electronic device, thereby increasing the accuracy of fake fingerprint detection. The technical problems to be achieved by the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person skilled in the art related to the present document from the description below.

[0099] An electronic device (e.g., an electronic device (101) of FIG. 1 or an electronic device (201) of FIG. 2) according to an embodiment of the present disclosure may include a display (e.g., a display module (160) of FIG. 1 or a display (260) of FIG. 2), a touch sensor (271) arranged to overlap with the display, a fingerprint sensor (273) arranged to overlap with the display, a memory (130, 230) for storing commands, and at least one processor (120, 220). The commands according to an embodiment, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain a first touch signal detected through the touch sensor and a first fingerprint image detected through the fingerprint sensor based on a first touch input through the display. The commands, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device. The commands, when individually or collectively executed by the at least one processor, may cause the electronic device to determine that the first fingerprint image is not fake if the first value of the first touch signal exceeds a first threshold value, and to determine that the first fingerprint image is fake if the value of the first touch signal is less than or equal to the first threshold value, when the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device.The above instructions, when executed individually or collectively by the at least one processor, may cause the electronic device to determine that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, and to determine that the first fingerprint image is counterfeit if the first value of the first touch signal is less than or equal to the second threshold value, when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device.

[0100] The instructions according to one embodiment, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a value corresponding to a touch signal by a dielectric including a fake fingerprint when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device as being smaller than a value corresponding to a touch signal by the dielectric including the fake fingerprint when the electronic device is gripped by the human body or the electronic device is electrically connected to the external electronic device.

[0101] The instructions according to one embodiment, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain a plurality of values ​​corresponding to the first touch signal using a touch map of the touch sensor. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine a maximum value among the plurality of values ​​corresponding to the first touch signal as the first value of the first touch signal, or to determine a sum of values ​​of a first portion greater than the remaining values ​​among the plurality of values ​​corresponding to the first touch signal as the first value of the first touch signal.

[0102] According to an embodiment, the electronic device may further include a gyro sensor (275). The commands according to an embodiment, when individually or collectively executed by the at least one processor, may cause the electronic device to identify that the electronic device is not gripped by the human body if a distribution range of values ​​sensed by the gyro sensor is narrower than a first distribution range. The commands, when individually or collectively executed by the at least one processor, may cause the electronic device to identify that the electronic device is gripped by the human body if the distribution range of values ​​sensed by the gyro sensor is equal to or wider than the first distribution range, or to identify that the electronic device is not gripped by the human body if a distribution range of differences between values ​​sensed at a time interval specified by the gyro sensor is narrower than a second distribution range, and to identify that the electronic device is gripped by the human body if the distribution range of the differences is equal to or wider than the second distribution range.

[0103] According to an embodiment, the electronic device may further include an acceleration sensor (277). The commands according to an embodiment, when individually or collectively executed by the at least one processor, may cause the electronic device to identify that the electronic device is not gripped by the human body if a distribution range of values ​​sensed by the acceleration sensor is narrower than a third distribution range. The commands, when individually or collectively executed by the at least one processor, may cause the electronic device to identify that the electronic device is gripped by the human body if the distribution range of values ​​sensed by the acceleration sensor is equal to or wider than the third distribution range, or to identify that the electronic device is not gripped by the human body if a distribution range of differences between values ​​sensed at a time interval specified by the acceleration sensor is narrower than a fourth distribution range. The above commands, when individually or collectively executed by the at least one processor, may cause the electronic device to identify that the electronic device is gripped by the human body if the distribution range of the difference values ​​is equal to or wider than the fourth distribution range.

[0104] The electronic device according to one embodiment may further include a connector (278). The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify that the electronic device is electrically connected to the external electronic device if a cable is connected to the connector. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify that the electronic device is not electrically connected to the external electronic device if the cable is not connected to the connector.

[0105] The instructions according to one embodiment, when individually or collectively executed by the at least one processor, may cause the electronic device to determine whether the fingerprint image is forged using an image learning-based forged fingerprint detection model when the values ​​sensed by the gyro sensor and / or the values ​​sensed by the acceleration sensor are invalid.

[0106] In one embodiment, the commands, when individually or collectively executed by the at least one processor, may cause the electronic device to acquire, through the touch sensor, a plurality of touch signals corresponding to a plurality of touch inputs of a specific user while the electronic device is in at least one of the first state in which the electronic device is gripped by the human body or the second state in which the electronic device is electrically connected to the external electronic device. The commands, when individually or collectively executed by the at least one processor, may cause the electronic device to determine the first threshold for the specific user by multiplying an average of at least some of the values ​​of the plurality of touch signals for which fingerprint authentication was successful by a first relationship constant.

[0107] The instructions according to one embodiment, when executed individually or collectively by the at least one processor, may cause the electronic device to determine the second threshold for the specific user by multiplying the average of at least some of the values ​​of the plurality of touch signals for which fingerprint authentication was successful by a second relationship constant different from the first relationship constant.

[0108] In one embodiment, the electronic device may include a first classifier trained to determine whether the first fingerprint image is forged based on whether the first value of the first touch signal is less than or equal to the first threshold value, and a second classifier trained to determine whether the first fingerprint image is forged based on whether the first value of the first touch signal is less than or equal to the second threshold value. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to apply the first classifier to the first value of the first touch signal when the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to apply the second classifier to the first value of the first touch signal when the electronic device is not gripped by the human body and is not electrically connected to the external electronic device.

[0109] FIG. 3 is a diagram showing examples of values ​​of touch signals by a dielectric including a fake fingerprint according to one embodiment.

[0110] Referring to FIG. 3, the horizontal axis according to one embodiment may represent time and the vertical axis may represent the value of the touch signal (e.g., the maximum value among the electrostatic capacity change values ​​of the touch area). According to one embodiment, when the electronic device (201) is not gripped by a human body and is not connected to an external electronic device, the value (312) of the touch signal by the dielectric for a fake fingerprint may be smaller than the value (314) of the touch signal by the dielectric for a fake fingerprint when the electronic device (201) is gripped by a human body or connected to an external electronic device. According to one embodiment, a processor (220) uses a first threshold value as a reference value for determining whether an electronic device (201) corresponds to a fake fingerprint when the electronic device (201) is gripped by a human body or connected to an external electronic device, and uses a second threshold value that is smaller than the first threshold value as a reference value for determining whether a value of a touch signal corresponds to a fake fingerprint when the electronic device (201) is not gripped by a human body or electrically connected to an external electronic device, thereby increasing the accuracy of determining whether a fingerprint is fake.

[0111] FIG. 4a is a diagram showing vertical TX lines and horizontal RX lines of a touch sensor according to one embodiment.

[0112] Referring to FIG. 4A, a touch sensor (271) according to an embodiment may include a touch screen panel using a capacitive method. The touch screen panel according to an embodiment may include TX lines (X channels) in a horizontal direction (e.g., X direction) and RX lines (Y channels) in a vertical direction (e.g., Y direction), and may form mutual capacitance (or self-capacitance) through the TX lines and the RX lines.

[0113] FIG. 4b is a drawing for explaining a change in electrostatic capacitance formed between a touch sensor and a finger according to one embodiment.

[0114] Referring to FIG. 4B, when a first touch input (401) by a user's body (e.g., a finger) occurs through the display (260) while a capacitance (410) is formed between the TX line and the RX line of the touch screen panel according to one embodiment, a part of the capacitance (410) formed between the TX line and the RX line may be taken away by the finger, resulting in a change (410) in the capacitance between the TX line and the RX line. The touch sensor (271) according to one embodiment may sense (or detect or obtain) the value of a touch signal (or the amount of change in the intensity of the touch signal) (e.g., the amount of change in the capacitance) based on the change (410) in the capacitance between the TX line and the RX line.

[0115] FIG. 5 is a diagram showing an example of obtaining a value of electrostatic capacitance between a TX line and an RX line according to one embodiment.

[0116] Referring to FIG. 5, the value of the electrostatic capacitance (C) between conductors (e.g., TX line and RX line) located at both ends of a dielectric (e.g., finger) according to one embodiment can be determined as the product of ε0 (e.g., permittivity of free space), εr (e.g., relative dielectric constant), and A (e.g., area of ​​plates) divided by d (e.g., distance between plates).

[0117] According to one embodiment, when attempting fingerprint authentication by attaching a fake fingerprint to a real fingerprint, the distance between the touch screen panel and the finger may increase by the thickness of the fake fingerprint. Since the increase in the distance between the dielectric and the conductors decreases the value of the electrostatic capacitance (C), the value (or electrostatic capacitance value) of the touch signal according to the touch input corresponding to the fake fingerprint may be smaller than the value (or electrostatic capacitance value) of the touch signal according to the touch input corresponding to the real fingerprint under the specified conditions.

[0118] FIG. 6 is a diagram showing touch maps for touch input corresponding to a real fingerprint and touch input corresponding to a fake fingerprint under the same conditions according to one embodiment.

[0119] Referring to FIG. 6, in each of the touch map (610) corresponding to a real fingerprint (real) and the touch map (620) corresponding to a spoof fingerprint (spoof) according to one embodiment, the horizontal direction may represent the x-coordinate and the vertical direction may represent the y-coordinate. The numbers (or boldness) within the grid spaces between the x and y coordinates may represent the value of the touch signal (e.g., the touch signal intensity value or the change in electrostatic capacity). According to one embodiment, if the maximum value of the plurality of values ​​(e.g., touch signal intensity value or change in electrostatic capacity) (612) of the touch signal in the touch map (610) when a touch input corresponding to a real fingerprint (real) is 903 under the same conditions (e.g., a state in which an electronic device is gripped), then the maximum value of the plurality of values ​​(e.g., touch signal intensity value or change in electrostatic capacity) (622) of the touch signal in the touch map (620) can be identified as 403 because the distance between the touch screen panel and the finger increases by the thickness of the spoof fingerprint when a touch input corresponding to a spoof fingerprint is increased. In this way, under the same conditions (e.g., a state in which an electronic device is gripped), the difference between the value of the touch signal (e.g., maximum value 903) when a touch input corresponding to a real fingerprint (real) and the value of the touch signal (e.g., maximum value 403) when a touch input corresponding to a spoof fingerprint is large, so that a spoof fingerprint can be determined using a single threshold value.

[0120] FIG. 7 is a diagram showing touch maps corresponding to actual fingerprints when a touch input is performed on an electronic device in a state where the electronic device is gripped by a human body and in a state where the electronic device is not gripped by a human body according to one embodiment.

[0121] Referring to FIG. 7, a touch map (710) is illustrated when a touch input corresponding to a real fingerprint (real) is performed when an electronic device (201) is gripped according to an embodiment, and a touch map (720) is illustrated when a touch input corresponding to a real fingerprint (real) is performed when the electronic device (201) is not gripped. In each of the touch maps (710, 720), the horizontal direction may represent an x-coordinate, and the vertical direction may represent a y-coordinate. The numbers (or boldness) within the grid spaces between the x and y coordinates may represent the value of a touch signal (e.g., a touch signal intensity value or a change in electrostatic capacity). According to one embodiment, when the electronic device (201) is gripped by a human body and a touch input corresponding to a real fingerprint is performed, the maximum value of the plurality of values ​​(e.g., touch signal intensity value or change in electrostatic capacity) (712) of the touch signal in the touch map (710) is 860, and when the electronic device (201) is not gripped by a human body and is not grounded, the maximum value of the plurality of values ​​(e.g., touch signal intensity value or change in electrostatic capacity) (622) of the touch signal in the touch map (720) can be identified as 416.

[0122] Referring to FIGS. 6 and 7, when a touch input corresponding to a spoof fingerprint (e.g., maximum value 403) is performed while the electronic device is gripped, the value of the touch signal (e.g., maximum value 416) is so similar to the value of the touch signal when a touch input corresponding to a real fingerprint (e.g., maximum value 416) is performed while the electronic device is not gripped that it may be difficult to distinguish them. In one embodiment, the processor (220) uses a first threshold value as a reference value for determining whether the electronic device (201) corresponds to a spoof fingerprint when it is gripped by a human body, and uses a second threshold value smaller than the first threshold value as a reference value for determining whether the value of the touch signal corresponds to a spoof fingerprint when the electronic device (201) is not gripped by a human body, thereby increasing the accuracy of determining whether or not a fingerprint is spoof.

[0123] FIG. 8 is a diagram illustrating touch maps corresponding to actual fingerprints when a touch input is performed in a state in which an electronic device is electrically connected to an external electronic device and in a state in which the electronic device is not electrically connected to an external electronic device according to one embodiment.

[0124] Referring to FIG. 8, a touch map (810) is illustrated when a touch input corresponding to a real fingerprint (real) is performed when an electronic device (201) is electrically connected to an external electronic device according to an embodiment, and a touch map (820) is illustrated when a touch input corresponding to a real fingerprint (real) is performed when the electronic device (201) is not electrically connected to the external electronic device. In each of the touch maps (810, 820), the horizontal direction may represent an x-coordinate, and the vertical direction may represent a y-coordinate. The numbers (or boldness) in the grid spaces between the x and y coordinates may represent the value of a touch signal (e.g., a touch signal intensity value or a change in electrostatic capacity). According to one embodiment, when the electronic device (201) is electrically connected to an external electronic device and a touch input corresponding to a real fingerprint is performed, the maximum value of the plurality of values ​​(e.g., touch signal intensity value or change in electrostatic capacity) (812) of the touch signal in the touch map (810) is 699, and when the electronic device (201) is not electrically connected to an external electronic device and is not grounded, the maximum value of the plurality of values ​​(e.g., touch signal intensity value or change in electrostatic capacity) (822) of the touch signal in the touch map (820) can be identified as 416.

[0125] According to one embodiment, when a touch input corresponding to a spoof fingerprint is performed while the electronic device is electrically connected to an external electronic device, the value of the touch signal may be very similar to the value (e.g., maximum value 416) of the touch signal when a touch input corresponding to a real fingerprint is performed while the electronic device is not electrically connected to the external electronic device, making it difficult to distinguish between them. According to one embodiment, the processor (220) uses a first threshold value as a reference value for determining whether the electronic device (201) corresponds to a spoof fingerprint while the electronic device (201) is electrically connected to the external electronic device, and uses a second threshold value smaller than the first threshold value as a reference value for determining whether the value of the touch signal corresponds to a spoof fingerprint while the electronic device (201) is not electrically connected to the external electronic device, thereby increasing the accuracy of determining whether or not a fingerprint is spoof.

[0126] FIG. 9 is a diagram showing three-dimensional touch maps according to the type of fake fingerprint according to one embodiment.

[0127] Referring to FIG. 9, a first three-dimensional touch map (910) is illustrated when a touch input corresponds to a first type of fake fingerprint, and a second three-dimensional touch map (920) is illustrated when a touch input corresponds to a second type of fake fingerprint. In each of the three-dimensional touch maps (910, 920), the x-axis may represent the x-coordinate of the touch panel screen, and the y-axis may represent the y-coordinate of the touch panel screen. The z-axis may represent the value of the touch signal (e.g., a touch signal intensity value or a change value of electrostatic capacitance).

[0128] Referring to the first three-dimensional touch map (910) according to an embodiment, when a genome including a first type of fake fingerprint is touched, the distribution form of the values ​​of the touch signal of the touch map can be expressed as the first form (912). Referring to the second three-dimensional touch map (920) according to an embodiment, when a genome including a second type of fake fingerprint is touched, the distribution form of the values ​​of the touch signal of the touch map (920) can be expressed as the second form (922). According to an embodiment, the processor (220) can train the first classifier and the second classifier on the distribution form of the touch map (e.g., the features of the values ​​of the 3D touch map (e.g., the shape of the graph by the values ​​of the 3D touch map (whether the center is sharp or flat), the slope of the graph by the values ​​of the 3D touch map (the magnitude of the slope at which the value of the touch signal decreases from the center to the periphery of the touch map), or the distribution of the values ​​of the 2D or 3D touch map (whether the touch signal is evenly distributed or concentrated in a few sizes)) that are expressed differently for each of a plurality of types of fake fingerprints, and can increase the accuracy of determining whether or not a fingerprint is fake by using the trained first classifier and the second classifier.

[0129] FIG. 10 is a diagram showing graphs of changes in the maximum value on the touch map over time according to the type of fake fingerprint according to one embodiment.

[0130] Referring to FIG. 10, a first graph (1010) according to an embodiment may be a graph showing a change over time of a maximum value on a touch map according to a touch input of a first genome corresponding to a first type of fake fingerprint. A second graph (1020) may be a graph showing a change over time of a maximum value on a touch map of a second genome corresponding to a second type of fake fingerprint. In the first graph (1010) and the second graph (1020), the x-axis may represent a frame. The first frame may mean a time interval corresponding to a display setting (e.g., 60 Hz or 120 Hz or other setting value). In the first graph (1010) and the second graph (1020), the y-axis may represent a maximum value on the touch map.

[0131] Referring to a first graph (1010) according to an embodiment, when a touch input of a first dielectric corresponding to a first type of fake fingerprint is performed, a change in the maximum value on the touch map between frames (e.g., over time) according to the first dielectric constant of the first dielectric may be represented as a first curve (1012). Referring to a second graph (1020) according to an embodiment, when a touch input of a second dielectric corresponding to a second type of fake fingerprint is performed, a change in the maximum value on the touch map between frames (e.g., over time) according to the second dielectric constant of the second dielectric may be represented as a second curve (1022). According to an embodiment, in the first graph (1010), a difference in the change in the maximum value on the touch map between frames may be at most 40, and in the second graph (1020), a difference in the change in the maximum value on the touch map between frames may be at most 140.

[0132] According to an embodiment, the processor (220) may utilize the fact that the change range (or change characteristic) of the maximum value on the touch map over time is different depending on the type of fake fingerprint to derive a constant value by converting the slope of the maximum value of the frame increasing or decreasing over time into an n-th order function depending on the type of fake fingerprint, or may calculate the difference in intensity between the first frame and the last frame, or may calculate the difference between the frame with the maximum value and the frame with the minimum value as the change range, and may train the first classifier and the second classifier with the calculated value, and may increase the accuracy of determining whether or not a fingerprint is fake by using the trained first classifier and the second classifier.

[0133] FIG. 11 is a diagram for explaining an example of determining a first threshold value and a second threshold value according to one embodiment.

[0134] Referring to FIG. 11, a processor (220) according to an embodiment may set a buffer for collecting a plurality of touch signals (e.g., a touch signal corresponding to each of 20 touch inputs). The processor (220) according to an embodiment may acquire touch signal values ​​(e.g., touch data) sequentially in a buffer whenever fingerprint recognition is successful in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device, and may store the latest touch data while deleting the oldest touch data when the buffer capacity is reached. In one embodiment, when the number of touch signals (e.g., touch data) acquired each time a fingerprint is successfully recognized in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device becomes an initially specified number (N (e.g., 10)), an initial first threshold value for a specific user may be determined by multiplying a first relationship constant by the average of the maximum values ​​of each of the N touch signals according to the above mathematical expression 1, and an initial second threshold value for a specific user may be determined by multiplying a second relationship constant by the average of the maximum values ​​of each of the N touch signals according to the above mathematical expression 2. According to one embodiment, when N+1 (e.g., 11) touch signals (or touch data) are acquired after determining the first threshold value and the first second threshold value and then the next fingerprint authentication is successful, the processor (220) may determine an updated first threshold value for a specific user by multiplying the first relationship constant by the average of the maximum values ​​of each of the 11 touch signals according to the above mathematical expression 1, and may determine an updated second threshold value for a specific user by multiplying the second relationship constant by the average of the maximum values ​​of each of the 11 touch signals according to the above mathematical expression 2.According to an embodiment, the processor (220) may update the first threshold value and the second threshold value using a moving average every time 2 to 20 touch signals are acquired after determining the first threshold value and the first second threshold value. According to an embodiment, the processor (220) may update the first threshold value and the second threshold value using the 20 touch signals that are most recently updated (e.g., the 2nd to 21st touch data or the 3rd to 22nd touch data) depending on the buffer capacity after 20.

[0135] Fig. 12a is a histogram showing the distribution of Yaw values ​​of a gyro sensor according to whether or not the electronic device is gripped by a human body according to one embodiment. Fig. 12b is a histogram showing the distribution of Pitch values ​​of a gyro sensor according to whether or not the electronic device is gripped by a human body according to one embodiment. Fig. 12c is a histogram showing the distribution of Roll values ​​of a gyro sensor according to whether or not the electronic device is gripped by a human body according to one embodiment.

[0136] First, referring to FIG. 12A, in a first histogram (1210) according to an embodiment, the x-axis may represent a yaw-axis value (e.g., a yaw-axis angular velocity value) of a gyro sensor (275), and the y-axis may represent the number of yaw-axis values. The gyro sensor (275) according to an embodiment may sense yaw-axis angular velocity values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1211). The gyro sensor (275) according to an embodiment may sense yaw-axis angular velocity values ​​in a wide distribution range when the electronic device (201) is gripped by a human body (e.g., a user's hand) (hand grip) (1213).

[0137] Referring to FIG. 12B, in a second histogram (1220) according to an embodiment, the x-axis may represent a pitch-axis value (e.g., a pitch-axis angular velocity value) of the gyro sensor (275), and the y-axis may represent the number of pitch-axis values. The gyro sensor (275) according to an embodiment may sense pitch-axis angular velocity values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1221). The gyro sensor (275) according to an embodiment may sense pitch-axis angular velocity values ​​in a wide distribution range when the electronic device (201) is gripped by a human body (e.g., a user's hand) (hand grip) (1223).

[0138] Referring to FIG. 12C, in a third histogram (1230) according to an embodiment, the x-axis may represent a roll-axis value (e.g., a roll-axis angular velocity value) of the gyro sensor (275), and the y-axis may represent the number of roll-axis values. The gyro sensor (275) according to an embodiment may sense roll-axis angular velocity values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1231). The gyro sensor (275) according to an embodiment may sense roll-axis angular velocity values ​​in a wide distribution range when the electronic device (201) is gripped by a human body (e.g., a user's hand) (hand grip) (1233).

[0139] Referring to FIGS. 12A, 12B, and 12C, the processor (220) according to an embodiment may determine that the electronic device (201) is not gripped by a human body if the distribution range of values ​​sensed by the gyro sensor (275) (e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) is narrower than a specified first distribution range. The processor (220) according to an embodiment may determine that the electronic device (201) is gripped by a human body if the distribution range of values ​​sensed by the gyro sensor (275) is equal to or wider than the first distribution range. According to one embodiment, the first distribution range may be determined as a distribution range that can distinguish between a narrow distribution of yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0140] Fig. 13a is a histogram showing the distribution of x-angle values ​​of an acceleration sensor (277) according to whether or not a human body grips an electronic device according to one embodiment. Fig. 13b is a histogram showing the distribution of y-angle values ​​of an acceleration sensor (277) according to whether or not a human body grips an electronic device according to one embodiment. Fig. 13c is a histogram showing the distribution of z-angle values ​​of an acceleration sensor (277) according to whether or not a human body grips an electronic device according to one embodiment.

[0141] First, referring to FIG. 13A, in a fourth histogram (1310) according to an embodiment, the x-axis may represent the x-angle value of the acceleration sensor (277), and the y-axis may represent the number of x-angle values. The acceleration sensor (277) according to an embodiment may sense x-angle values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1311). The acceleration sensor (277) according to an embodiment may sense x-angle values ​​in a wide distribution range when the electronic device (201) is gripped by a human body (e.g., a user's hand) (hand grip) (1313).

[0142] Referring to FIG. 13B, in a fifth histogram (1320) according to an embodiment, the x-axis may represent the y-angle value of the acceleration sensor (275), and the y-axis may represent the number of y-angle values. The acceleration sensor (277) according to an embodiment may sense y-angle values ​​of different distribution ranges when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1321) and when the electronic device (201) is gripped by a human body (e.g., a user's hand) (hand grip) (1323).

[0143] Referring to FIG. 13c, in a sixth histogram (1330) according to an embodiment, the x-axis may represent the z-angle value of the acceleration sensor (275), and the y-axis may represent the number of z-angle values. The acceleration sensor (277) according to an embodiment may sense z-angle values ​​of different distribution ranges when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1331) and when the electronic device (201) is gripped by a human body (e.g., a user's hand) (hand grip) (1333).

[0144] Referring to FIGS. 13A, 13B, and 13C, a processor (220) according to an embodiment may determine that the electronic device (201) is not gripped by a human body when the distribution range of the x-angle values ​​or the distribution range of the xyz composite values ​​synthesized by the x-angle values, y-angle values, and z-angle values ​​is narrower than a designated third distribution range by using the distribution range of the values ​​sensed by the acceleration sensor (275) (e.g., x-angle values, y-angle values, or z-angle values). According to one embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body when the distribution range of the x-angle values ​​or the distribution range of the xyz composite values ​​synthesized by the x-angle values, y-angle values, and z-angle values ​​is equal to or wider than the third distribution range by using the distribution range of the values ​​sensed by the acceleration sensor (275) (e.g., x-angle values, y-angle values, or z-angle values). According to one embodiment, the third distribution range can be determined as a distribution range that can distinguish between a narrow distribution of x-angle values ​​using values ​​(e.g., x-angle values, y-angle values, or z-angle values) sensed by the acceleration sensor (275) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) or xyz composite values ​​synthesized from x-angle values, y-angle values, and z-angle values ​​and a wide distribution of x-angle values ​​using values ​​(e.g., x-angle values, y-angle values, or z-angle values) sensed by the acceleration sensor (275) when the electronic device (201) is gripped by a human body (e.g., a user's hand) or xyz composite values ​​synthesized from x-angle values, y-angle values, and z-angle values.

[0145] Fig. 14a is a histogram showing the distribution of difference values ​​(e.g., changes) of Yaw values ​​of a gyro sensor at a specified time interval depending on whether the electronic device is gripped by a human body according to an embodiment of the present invention. Fig. 14b is a histogram showing the distribution of difference values ​​(changes) of Pitch values ​​of a gyro sensor at a specified time interval depending on whether the electronic device is gripped by a human body according to an embodiment of the present invention. Fig. 14c is a histogram showing the distribution of difference values ​​(changes) of Roll values ​​of a gyro sensor at a specified time interval depending on whether the electronic device is gripped by a human body according to an embodiment of the present invention.

[0146] First, referring to FIG. 14A, in a seventh histogram (1410) according to an embodiment, the x-axis may represent differences in yaw-axis values ​​(e.g., yaw-axis angular velocity values) at a specified time interval of the gyro sensor (275), and the y-axis may represent the number of difference values. The processor (220) according to an embodiment may obtain first yaw-axis angular velocity values ​​through the gyro sensor (275), obtain second yaw-axis angular velocity values ​​after a specified time, and detect differences (or changes) between the first yaw-axis angular velocity values ​​and the second yaw-axis angular velocity values. The processor (220) according to an embodiment may identify differences in yaw-axis values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1411). According to one embodiment, the processor (220) can identify differences in yaw axis values ​​in a wide range of distributions when the electronic device (201) is gripped by a human body (e.g., a user's hand) (no grip) (1413).

[0147] Referring to FIG. 14B, in the eighth histogram (1420) according to an embodiment, the x-axis may represent differences in pitch-axis values ​​(e.g., pitch-axis angular velocity values) at a specified time interval of the gyro sensor (275), and the y-axis may represent the number of difference values. The processor (220) according to an embodiment may obtain first pitch-axis angular velocity values ​​through the gyro sensor (275), obtain second pitch-axis angular velocity values ​​after a specified time, and detect differences (or changes) between the first pitch-axis angular velocity values ​​and the second pitch-axis angular velocity values. The processor (220) according to an embodiment may identify differences in pitch-axis values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1421). According to one embodiment, the processor (220) can identify differences in pitch axis values ​​in a wide distribution range when the electronic device (201) is gripped by a human body (e.g., a user's hand) (no grip) (1423).

[0148] Referring to FIG. 14C, in a ninth histogram (1430) according to an embodiment, the x-axis may represent differences in roll-axis values ​​(e.g., roll-axis angular velocity values) at a specified time interval of the gyro sensor (275), and the y-axis may represent the number of difference values. The processor (220) according to an embodiment may obtain first roll-axis angular velocity values ​​through the gyro sensor (275), obtain second roll-axis angular velocity values ​​after a specified time, and detect differences (or changes) between the first roll-axis angular velocity values ​​and the second roll-axis angular velocity values. The processor (220) according to an embodiment may identify differences in roll-axis values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1431). According to one embodiment, the processor (220) can identify differences in roll axis values ​​in a wide distribution range when the electronic device (201) is gripped by a human body (e.g., a user's hand) (no grip) (1433).

[0149] Referring to FIGS. 14A, 14B, and 14C, the processor (220) according to an embodiment may determine that the electronic device (201) is not gripped by a human body if the distribution range of the differences in yaw-axis values, pitch-axis values, or roll-axis values ​​by the gyro sensor (275) is narrower than a designated second distribution range. The processor (220) according to an embodiment may determine that the electronic device (201) is gripped by a human body if the distribution range of the differences in yaw-axis values, pitch-axis values, or roll-axis values ​​by the gyro sensor (275) is equal to or wider than the second distribution range. According to one embodiment, the second distribution range may be determined as a distribution range that can distinguish between a narrow distribution of differences in yaw-axis values, differences in pitch-axis values, or differences in roll-axis values ​​by the gyro sensor (275) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of differences in yaw-axis values, differences in pitch-axis values, or differences in roll-axis values ​​by the gyro sensor (275) when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0150] FIG. 15a is a histogram showing the distribution of difference values ​​(change amounts) of x-angle values ​​of an acceleration sensor at a specified time interval depending on whether a human body grips the electronic device according to an embodiment. FIG. 15b is a histogram showing the distribution of difference values ​​(change amounts) of y-angle values ​​of an acceleration sensor at a specified time interval depending on whether a human body grips the electronic device according to an embodiment. FIG. 15c is a histogram showing the distribution of difference values ​​(change amounts) of z-angle values ​​of a gyro sensor at a specified time interval depending on whether a human body grips the electronic device according to an embodiment.

[0151] First, referring to FIG. 15A, in a tenth histogram (1510) according to an embodiment, the x-axis may represent differences in x-angle values ​​at a specified time interval of the acceleration sensor (277), and the y-axis may represent the number of difference values. The processor (220) according to an embodiment may obtain first x-angle values ​​through the acceleration sensor (277), obtain second x-angle values ​​after a specified time, and detect differences (or changes) between the first x-angle values ​​and the second x-angle values. The processor (220) according to an embodiment may identify differences in x-angle values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1511). According to one embodiment, the processor (220) can identify differences in x-angle values ​​in a wide range of distributions when the electronic device (201) is gripped by a human body (e.g., a user's hand) (no grip) (1513).

[0152] Referring to FIG. 15B, in the eleventh histogram (1520) according to an embodiment, the x-axis may represent differences in y-angle values ​​at a specified time interval of the acceleration sensor (277), and the y-axis may represent the number of difference values. The processor (220) according to an embodiment may obtain first y-angle values ​​through the acceleration sensor (277), obtain second y-angle values ​​after a specified time, and detect differences (or changes) between the first y-angle values ​​and the second y-angle values. The processor (220) according to an embodiment may identify differences in y-angle values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1521). According to one embodiment, the processor (220) can identify differences in y-angle values ​​in a wide range of distributions when the electronic device (201) is gripped by a human body (e.g., a user's hand) (no grip) (1523).

[0153] Referring to FIG. 15c, in a 12th histogram (1530) according to an embodiment, the x-axis may represent differences in z-angle values ​​at a specified time interval of the acceleration sensor (277), and the y-axis may represent the number of difference values. The processor (220) according to an embodiment may obtain first z-angle values ​​through the acceleration sensor (277), obtain second z-angle values ​​after a specified time, and detect differences (or changes) between the first z-angle values ​​and the second z-angle values. The processor (220) according to an embodiment may identify differences in z-angle values ​​in a narrow distribution range close to 0 when the electronic device (201) is not gripped by a human body (e.g., a user's hand) (no grip) (1531). According to one embodiment, the processor (220) can identify differences in z-angle values ​​in a wide range of distributions when the electronic device (201) is gripped by a human body (e.g., a user's hand) (no grip) (1533).

[0154] Referring to FIGS. 15A, 15B, and 15C, the processor (220) according to an embodiment may determine that the electronic device (201) is not gripped by a human body if the distribution range of the differences in x-angle values, y-angle values, or z-angle values ​​by the acceleration sensor (277) is narrower than a designated fourth distribution range. The processor (220) according to an embodiment may determine that the electronic device (201) is gripped by a human body if the distribution range of the differences in x-angle values, y-angle values, or z-angle values ​​by the acceleration sensor (277) is equal to or wider than the fourth distribution range. According to one embodiment, the fourth distribution range may be determined as a distribution range that can distinguish between a narrow distribution of differences in x-angle values, differences in y-angle values, or differences in z-angle values ​​by the acceleration sensor (277) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of differences in x-angle values, differences in y-angle values, or differences in z-angle values ​​by the acceleration sensor (277) when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0155] Figure 16 is a flowchart illustrating a fake fingerprint detection operation in an electronic device according to one embodiment. In the embodiment of Figure 16, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0156] Referring to FIG. 16, a processor (220) of an electronic device (201) (e.g., the electronic device (101) of FIG. 1) according to one embodiment may perform at least one of operations 1610 to 1644.

[0157] In operation 1610, a processor (220) according to an embodiment may obtain a first touch signal (e.g., a signal corresponding to a first electrostatic capacitance change value) corresponding to the first touch input through a touch sensor (271) based on a touch input (e.g., a first touch input) through a display (260) (e.g., when a touch input is made to obtain a user's fingerprint image), and may obtain a first fingerprint image corresponding to the first touch input through a fingerprint sensor (273).

[0158] In operation 1620, the processor (220) according to an embodiment may identify a value of the first touch signal, whether the electronic device (201) is gripped by a human body (e.g., a user's hand), and whether the electronic device (201) is electrically connected to an external device based on the acquisition of the first touch signal and the first fingerprint image. The processor (220) according to an embodiment may obtain a plurality of values ​​on the touch map corresponding to the first touch signal using the touch map of the touch sensor (271). The touch map according to an embodiment may be a map representing x, y coordinates mapped so that each location (x, y coordinates) of the display (260) (e.g., a display panel) and each location (x, y coordinates) of the touch sensor (271) (e.g., a touch screen panel) are matched. According to an embodiment, a processor (220) may obtain a first touch signal sensed by a touch sensor (271) in response to a first touch input in which a portion of the entire area of ​​a display (260) is touched by a user. According to an embodiment, the processor (220) may identify a plurality of coordinate values ​​of the touched area using a touch map based on the acquisition of the first touch signal, and obtain a plurality of values ​​(e.g., a plurality of touch signal intensity values) corresponding to each of the coordinate values. According to an embodiment, the processor (220) may determine a maximum value among the plurality of touch signal intensity values ​​as the value of the first touch signal, or may determine a sum of some touch signal intensity values ​​having a greater value than the remaining portions among the plurality of touch signal intensity values ​​as the first value of the first touch signal.

[0159] In operation 1630, the processor (220) according to one embodiment can identify whether the electronic device (201) is gripped by a human body (e.g., a user's hand) or whether the electronic device (201) is connected to an external electronic device.

[0160] According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body if the distribution range of values ​​sensed by the gyro sensor (275) (e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) is narrower than a specified first distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of values ​​sensed by the gyro sensor (275) is equal to or wider than the first distribution range. According to an embodiment, if the electronic device (201) is not gripped by a human body (e.g., a user's hand), the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) may be distributed in a narrow distribution range close to 0. According to one embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) may be distributed in a wide distribution range. According to one embodiment, the first distribution range may be determined as a distribution range that can distinguish between a narrow distribution of the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0161] According to an embodiment, the processor (220) may obtain first sensed values ​​(e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) by the gyro sensor (275), obtain second sensed values ​​(e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) by the gyro sensor (275) after a specified time, and detect differences (or changes) between the first sensed values ​​and the second sensed values. According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body when a distribution range of differences (or changes) between the first sensed values ​​and the second sensed values ​​is narrower than a specified second distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the differences (or changes) between the first sensed values ​​and the second sensed values ​​is equal to or wider than the second distribution range. According to an embodiment, when the electronic device (201) is not gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a narrow distribution range close to 0. According to an embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a wide distribution range. According to one embodiment, the second distribution range may be determined as a distribution range that can distinguish between a narrow distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0162] According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body if the distribution range of the values ​​(e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) sensed by the acceleration sensor (277) is narrower than a designated third distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the values ​​sensed by the acceleration sensor (277) is equal to or wider than the third distribution range. According to an embodiment, if the electronic device (201) is not gripped by a human body (e.g., a user's hand), the x-angle values ​​sensed by the acceleration sensor (277) or the xyz composite values ​​synthesized from the x-angle values, y-angle values, and z-angle values ​​may be distributed in a narrow distribution range close to 0. According to one embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the x-angle values ​​sensed by the acceleration sensor (277) or the xyz composite values ​​synthesized from the x-angle values, y-angle values, and z-angle values ​​may be distributed over a wide distribution range. According to one embodiment, the third distribution range can be determined as a distribution range that can distinguish between a narrow distribution of x-angle values ​​sensed by the acceleration sensor (277) or xyz composite values ​​synthesized from x-angle values, y-angle values, and z-angle values ​​when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of x-angle values ​​sensed by the acceleration sensor (277) or xyz composite values ​​synthesized from x-angle values, y-angle values, and z-angle values ​​when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0163] According to an embodiment, the processor (220) may obtain first sensed values ​​(e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) by the acceleration sensor (277), obtain second sensed values ​​(e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) by the acceleration sensor (277) after a specified time, and detect differences (or changes) between the first sensed values ​​and the second sensed values. According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body when a distribution range of differences (or changes) between the first sensed values ​​and the second sensed values ​​is narrower than a specified fourth distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the differences (or changes) between the first sensed values ​​and the second sensed values ​​is equal to or wider than the fourth distribution range. According to an embodiment, when the electronic device (201) is not gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a narrow distribution range close to 0. According to an embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a wide distribution range. According to one embodiment, the fourth distribution range may be determined as a distribution range that can distinguish between a narrow distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0164] According to one embodiment, the processor (220) may determine whether the electronic device (201) is gripped by a human body by using some or all of the following: a distribution range of values ​​sensed by the gyro sensor (275) (e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values), a distribution range of differences (or changes) between first sensed values ​​and second sensed values ​​by the gyro sensor (275), a distribution range of values ​​sensed by the acceleration sensor (277) (e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values), and a distribution range of differences (or changes) between first sensed values ​​and second sensed values ​​by the acceleration sensor (277). According to one embodiment, the processor (220) may use a grip sensor instead of a gyro sensor (275) and an acceleration sensor (277) to determine whether the electronic device (201) is gripped by a human body.

[0165] In one embodiment, the processor (220) may identify that the electronic device (201) is electrically connected to an external device (e.g., electronic device (102)) if a cable (e.g., a wired cable) (or a USB device or a charging device) is connected to the connector (278). In one embodiment, the processor (220) may identify that the electronic device (201) is not electrically connected to the external device if a cable (or a USB device or a charging device) is not connected to the connector (278).

[0166] According to an embodiment, the processor (220) may perform operation 1640 when the electronic device (201) is in at least one of a state in which the electronic device (201) is gripped by a human body (e.g., a first state) or a state in which the electronic device is electrically connected to an external electronic device (e.g., a second state). According to an embodiment, the processor (220) may perform operation 1650 when the electronic device (201) is not gripped by a human body and is not electrically connected to an external electronic device.

[0167] In operation 1640, the processor (220) according to an embodiment may identify whether the value of the first touch signal (e.g., the first value) exceeds a first threshold value. The first threshold value according to an embodiment may be a specified value. The processor (220) according to an embodiment may multiply the average of at least some values ​​for which fingerprint authentication is successful among the values ​​of a plurality of touch signals obtained in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device by a first relationship constant to designate (or determine) the first threshold value for a specific user. The first relationship constant according to an embodiment may be a constant value that is derived in advance through an experiment in at least one of the first state in which the electronic device (201) is gripped by a human body or the second state in which the electronic device (201) is electrically connected to an external electronic device. According to one embodiment, the processor (220) may perform operation 1642 if the value of the first touch signal (e.g., the first value) exceeds the first threshold value. According to one embodiment, the processor (220) may perform operation 1644 if the value of the first touch signal (e.g., the first value) is equal to or less than the first threshold value.

[0168] In operation 1642, the processor (220) according to one embodiment may determine (or determine or identify) that the first fingerprint image is not forged. If the first image is not forged, the processor (220) according to one embodiment may display information indicating this on the display (260). If the first image is not forged, the processor (220) according to one embodiment may perform user authentication for the first fingerprint image. If the first image is not forged, the processor (220) according to one embodiment may display a user authentication result for the first fingerprint image (e.g., a screen indicating successful user authentication) on the display (260).

[0169] In operation 1644, the processor (220) according to one embodiment may determine (or determine or identify) that the first fingerprint image is counterfeit. If the first image is counterfeit, the processor (220) according to one embodiment may display information indicating this and / or information indicating a user authentication failure on the display (260).

[0170] In operation 1650, the processor (220) according to an embodiment may identify whether the value of the first touch signal (e.g., the first value) exceeds a second threshold value. The second threshold value according to an embodiment may be a specified value. The processor (220) according to an embodiment may multiply the average of at least some values ​​for which fingerprint authentication is successful among the values ​​of a plurality of touch signals obtained in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device by a second relationship constant to designate (or determine) the second threshold value for a specific user. The second relationship constant according to an embodiment may be a constant value that is derived in advance by an experiment in at least one of the first state in which the electronic device (201) is gripped by a human body or the second state in which the electronic device (201) is electrically connected to an external electronic device. According to one embodiment, the processor (220) may perform operation 1642 if the value of the first touch signal (e.g., the first value) exceeds the second threshold value. According to one embodiment, the processor (220) may perform operation 1644 if the value of the first touch signal (e.g., the first value) is equal to or less than the second threshold value.

[0171] A method for detecting a fake fingerprint in an electronic device (e.g., the electronic device (101) of FIG. 1 or the electronic device (201) of FIG. 2) according to an embodiment of the present disclosure may include an operation of acquiring a first touch signal detected through a touch sensor (271) and a first fingerprint image detected through a fingerprint sensor (273) based on a first touch input through a display of the electronic device (e.g., the display module (160) of FIG. 1 or the display (260) of FIG. 2). The method may include an operation of identifying a first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device. The method may include an operation of determining that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a first threshold value when the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device, and an operation of determining that the first fingerprint image is counterfeit if the value of the first touch signal is less than or equal to the first threshold value. The method may include an operation of determining that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, and an operation of determining that the first fingerprint image is counterfeit if the first value of the first touch signal is less than or equal to the second threshold value when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device.

[0172] The method according to one embodiment may include an operation of identifying a value corresponding to a touch signal by a dielectric including a fake fingerprint when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device as being smaller than a value corresponding to a touch signal by the dielectric including the fake fingerprint when the electronic device is gripped by the human body or the electronic device is electrically connected to the external electronic device.

[0173] According to one embodiment, the method may include an operation of obtaining a plurality of values ​​corresponding to the first touch signal using a touch map of the touch sensor. The method may include an operation of determining a maximum value among the plurality of values ​​corresponding to the first touch signal as the first value of the first touch signal, or determining a sum of values ​​of a first portion greater than the remaining values ​​among the plurality of values ​​corresponding to the first touch signal as the first value of the first touch signal.

[0174] According to an embodiment, the method may include an operation of identifying that the electronic device is not gripped by the human body when a distribution range of values ​​sensed by the gyro sensor (275) of the electronic device is narrower than a first distribution range. The method may include an operation of identifying that the electronic device is gripped by the human body when the distribution range of values ​​sensed by the gyro sensor is equal to or wider than the first distribution range. The method may include an operation of identifying that the electronic device is not gripped by the human body when a distribution range of differences between values ​​sensed at a time interval specified by the gyro sensor is narrower than a second distribution range. The method may include an operation of identifying that the electronic device is gripped by the human body when a distribution range of the differences is equal to or wider than the second distribution range.

[0175] According to an embodiment, the method may include an operation of identifying that the electronic device is not gripped by the human body when a distribution range of values ​​sensed by the acceleration sensor (277) of the electronic device is narrower than a third distribution range. The method may include an operation of identifying that the electronic device is gripped by the human body when the distribution range of values ​​sensed by the acceleration sensor is equal to or wider than the third distribution range. The method may include an operation of identifying that the electronic device is not gripped by the human body when a distribution range of differences between values ​​sensed at a specified time interval by the acceleration sensor is narrower than a fourth distribution range. The method may include an operation of identifying that the electronic device is gripped by the human body when a distribution range of the differences is equal to or wider than the fourth distribution range.

[0176] The method according to one embodiment may include an operation for identifying the electronic device as being electrically connected to the external electronic device if a cable is connected to the connector (278) of the electronic device. The method may include an operation for identifying the electronic device as not being electrically connected to the external electronic device if the cable is not connected to the connector.

[0177] According to one embodiment, the method may include an operation of determining whether the fingerprint image is forged using an image learning-based forged fingerprint detection model when the values ​​sensed by the gyro sensor and / or the values ​​sensed by the acceleration sensor are invalid.

[0178] According to one embodiment, the method may include an operation of acquiring a plurality of touch signals corresponding to a plurality of touch inputs of a specific user through the touch sensor while the electronic device is in at least one of the first state in which the electronic device is gripped by the human body or the second state in which the electronic device is electrically connected to the external electronic device. The method may include an operation of determining the first threshold value for the specific user by multiplying an average of at least some of the values ​​of the plurality of touch signals for which fingerprint authentication is successful by a first relationship constant.

[0179] The method according to one embodiment may include an operation of determining the second threshold value for the specific user by multiplying the average of at least some of the values ​​of the plurality of touch signals for which fingerprint authentication was successful by a second relationship constant different from the first relationship constant.

[0180] FIG. 17A is a flowchart illustrating a fake fingerprint detection operation according to the validity of gyro sensor or acceleration sensor data when a fingerprint is input in an electronic device according to one embodiment. FIG. 17B is a flowchart illustrating the operation that follows FIG. 17A. In the embodiments of FIGS. 17A and 17B , the operations may be performed sequentially, but are not necessarily performed sequentially. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0181] Referring to FIGS. 17A and 17B , a processor (220) of an electronic device (201) (e.g., the electronic device (101) of FIG. 1 ) according to one embodiment may perform at least one of operations 1710 to 1790.

[0182] In operation 1710, a processor (220) according to an embodiment may obtain a first touch signal (e.g., a signal corresponding to a first electrostatic capacitance change value) corresponding to the first touch input through a touch sensor (271) based on a touch input (e.g., a first touch input) through a display (260) (e.g., when a touch input is made to obtain a user's fingerprint image), and may obtain a first fingerprint image corresponding to the first touch input through a fingerprint sensor (273).

[0183] In operation 1720, the processor (220) according to one embodiment may identify (or determine or determine) whether a fingerprint template matching the first image exists based on the first touch signal and the acquisition of the first fingerprint image. If a fingerprint template matching the first image exists, the processor (220) according to one embodiment may perform operation 1730, and if a fingerprint template matching the first image does not exist, the processor (220) may perform operation 1790.

[0184] In operation 1730, the processor (220) according to one embodiment may obtain a value of a touch signal and data for determining whether the electronic device (201) is gripped by a human body or electrically connected to an external electronic device. For example, the data for determining whether the electronic device (201) is gripped by a human body may include sensor data by a gyro sensor (275) (e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) and / or sensor data by an acceleration sensor (277) (e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values). For example, the data for determining whether the electronic device (201) is connected to an external electronic device may include data indicating whether a cable (e.g., a wired cable) (or a USB device or a charging device) is connected to a connector (278).

[0185] In operation 1740, the processor (220) according to an embodiment may determine whether the acquired data is valid. According to an embodiment, if the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are invalid, the result of the identification of whether the electronic device (201) is gripped by a human body (e.g., a user's hand) by the processor (220) may be an error. According to an embodiment, the processor (220) may determine whether the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are included in a specified valid value range, and if the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are included in the valid value range, the acquired data may be determined to be valid. According to an embodiment, the processor (220) may determine that the acquired data is valid if the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are included in the valid value range and may perform operation 1760 of FIG. 17B. According to an embodiment, the processor (220) may determine that the acquired data is invalid if the values ​​sensed from the gyro sensor (275) and / or the acceleration sensor (277) are not included in the valid value range.

[0186] In operation 1750, the processor (220) according to one embodiment may apply the first fingerprint image to a fake fingerprint judgment model that learns the real fingerprint image and the fake fingerprint image to determine whether the first fingerprint image is a fake fingerprint if the acquired data is invalid.

[0187] In operation 1752, the processor (220) according to one embodiment may determine whether the fingerprint image is determined to be counterfeit. If the fingerprint image is not determined to be counterfeit, the processor (220) according to one embodiment may perform operation 1780, and if the fingerprint image is determined to be counterfeit, the processor (220) according to one embodiment may perform operation 1790.

[0188] In operation 1760, the processor (220) according to one embodiment can identify whether the electronic device (201) is gripped by a human body (e.g., a user's hand) or whether the electronic device (201) is connected to an external electronic device.

[0189] According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body if the distribution range of values ​​sensed by the gyro sensor (275) (e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) is narrower than a specified first distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of values ​​sensed by the gyro sensor (275) is equal to or wider than the first distribution range. According to an embodiment, if the electronic device (201) is not gripped by a human body (e.g., a user's hand), the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) may be distributed in a narrow distribution range close to 0. According to one embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) may be distributed in a wide distribution range. According to one embodiment, the first distribution range may be determined as a distribution range that can distinguish between a narrow distribution of the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of the yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values ​​sensed by the gyro sensor (275) when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0190] According to an embodiment, the processor (220) may obtain first sensed values ​​(e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) by the gyro sensor (275), obtain second sensed values ​​(e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values) by the gyro sensor (275) after a specified time, and detect differences (or changes) between the first sensed values ​​and the second sensed values. According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body when a distribution range of differences (or changes) between the first sensed values ​​and the second sensed values ​​is narrower than a specified second distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the differences (or changes) between the first sensed values ​​and the second sensed values ​​is equal to or wider than the second distribution range. According to an embodiment, when the electronic device (201) is not gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a narrow distribution range close to 0. According to an embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a wide distribution range. According to one embodiment, the second distribution range may be determined as a distribution range that can distinguish between a narrow distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0191] According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body if the distribution range of the x-angle values ​​using the values ​​sensed by the acceleration sensor (277) (e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) or the xyz composite values ​​synthesized from the x-angle values, y-angle values, and z-angle values ​​is narrower than a designated third distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the x-angle values ​​using the values ​​sensed by the acceleration sensor (277) or the xyz composite values ​​synthesized from the x-angle values, y-angle values, and z-angle values ​​is equal to or wider than the third distribution range. According to one embodiment, when the electronic device (201) is not gripped by a human body (e.g., a user's hand), the x-axis acceleration values ​​sensed by the acceleration sensor (277) or the xyz-axis composite values ​​synthesized from the x-axis acceleration values, y-axis acceleration values, and z-axis acceleration values ​​may be distributed in a narrow distribution range close to 0. According to one embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the x-axis acceleration values ​​sensed by the acceleration sensor (277) or the xyz-axis composite values ​​synthesized from the x-axis acceleration values, y-axis acceleration values, and z-axis acceleration values ​​may be distributed in a wide distribution range.According to one embodiment, the third distribution range can be determined as a distribution range that can distinguish between a narrow distribution of x-axis acceleration values ​​sensed by the acceleration sensor (277) when the electronic device (201) is not gripped by a human body (e.g., a user's hand) or xyz-axis composite values ​​synthesized from x-axis acceleration values, y-axis acceleration values, and z-axis acceleration values, and a wide distribution of x-axis acceleration values ​​sensed by the acceleration sensor (277) when the electronic device (201) is gripped by a human body (e.g., a user's hand) or xyz-axis composite values ​​synthesized from x-axis acceleration values, y-axis acceleration values, and z-axis acceleration values.

[0192] According to an embodiment, the processor (220) may obtain first sensed values ​​(e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) by the acceleration sensor (277), obtain second sensed values ​​(e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values) by the acceleration sensor (277) after a specified time, and detect differences (or changes) between the first sensed values ​​and the second sensed values. According to an embodiment, the processor (220) may determine that the electronic device (201) is not gripped by a human body when a distribution range of differences (or changes) between the first sensed values ​​and the second sensed values ​​is narrower than a specified fourth distribution range. According to an embodiment, the processor (220) may determine that the electronic device (201) is gripped by a human body if the distribution range of the differences (or changes) between the first sensed values ​​and the second sensed values ​​is equal to or wider than the fourth distribution range. According to an embodiment, when the electronic device (201) is not gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a narrow distribution range close to 0. According to an embodiment, when the electronic device (201) is gripped by a human body (e.g., a user's hand), the differences (or changes) between the first sensed values ​​and the second sensed values ​​may be distributed in a wide distribution range. According to one embodiment, the fourth distribution range may be determined as a distribution range that can distinguish between a narrow distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is not gripped by a human body (e.g., a user's hand) and a wide distribution of differences (or changes) between the first sensed values ​​and the second sensed values ​​when the electronic device (201) is gripped by a human body (e.g., a user's hand).

[0193] According to one embodiment, the processor (220) may determine whether the electronic device (201) is gripped by a human body by using some or all of the following: a distribution range of values ​​sensed by the gyro sensor (275) (e.g., yaw-axis angular velocity values, pitch-axis angular velocity values, or roll-axis angular velocity values), a distribution range of differences (or changes) between first sensed values ​​and second sensed values ​​by the gyro sensor (275), a distribution range of values ​​sensed by the acceleration sensor (277) (e.g., x-axis acceleration values, y-axis acceleration values, or z-axis acceleration values), and a distribution range of differences (or changes) between first sensed values ​​and second sensed values ​​by the acceleration sensor (277). According to one embodiment, the processor (220) may use a grip sensor instead of a gyro sensor (275) and an acceleration sensor (277) to determine whether the electronic device (201) is gripped by a human body.

[0194] In one embodiment, the processor (220) may identify that the electronic device (201) is electrically connected to an external device (e.g., electronic device (102)) if a cable (e.g., a wired cable) (or a USB device or a charging device) is connected to the connector (278). In one embodiment, the processor (220) may identify that the electronic device (201) is not electrically connected to the external device if a cable (or a USB device or a charging device) is not connected to the connector (278).

[0195] According to an embodiment, the processor (220) may perform operation 1762 when the electronic device (201) is in at least one of a state in which the electronic device (201) is gripped by a human body (e.g., a first state) or a state in which the electronic device is electrically connected to an external electronic device (e.g., a second state). According to an embodiment, the processor (220) may perform operation 1770 when the electronic device (201) is not gripped by a human body and is not electrically connected to an external electronic device.

[0196] In operation 1762, the processor (220) according to an embodiment may identify whether the value of the first touch signal (e.g., the first value) exceeds a first threshold value. The first threshold value according to an embodiment may be a specified value. The processor (220) according to an embodiment may multiply the average of at least some values ​​for which fingerprint authentication is successful among the values ​​of a plurality of touch signals obtained in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device by a first relationship constant to designate (or determine) the first threshold value for a specific user. The first relationship constant according to an embodiment may be a constant value that is derived in advance through an experiment in at least one of the first state in which the electronic device (201) is gripped by a human body or the second state in which the electronic device (201) is electrically connected to an external electronic device. According to one embodiment, the processor (220) may perform operation 1764 if the value of the first touch signal (e.g., the first value) exceeds the first threshold value. According to one embodiment, the processor (220) may perform operation 1766 if the value of the first touch signal (e.g., the first value) is equal to or less than the first threshold value.

[0197] In operation 1764, the processor (220) according to one embodiment may determine (or determine or identify) that the first fingerprint image is not forged. If the first image is not forged, the processor (220) according to one embodiment may display information indicating this on the display (260). If the first image is not forged, the processor (220) according to one embodiment may perform user authentication for the first fingerprint image. If the first image is not forged, the processor (220) according to one embodiment may display a user authentication result for the first fingerprint image (e.g., a screen indicating successful user authentication) on the display (260).

[0198] In operation 1766, the processor (220) according to one embodiment may determine (or determine or identify) that the first fingerprint image is counterfeit. If the first image is counterfeit, the processor (220) according to one embodiment may display information indicating this and / or information indicating a user authentication failure on the display (260).

[0199] In operation 1770, the processor (220) according to one embodiment may identify whether the value of the first touch signal (e.g., the first value) exceeds a second threshold value. The second threshold value according to one embodiment may be a specified value. The processor (220) according to one embodiment may multiply the average of at least some values ​​for which fingerprint authentication is successful among the values ​​of a plurality of touch signals obtained in at least one of a first state in which the electronic device (201) is gripped by a human body or a second state in which the electronic device (201) is electrically connected to an external electronic device by a second relationship constant to designate (or determine) the second threshold value for a specific user. The second relationship constant according to one embodiment may be a constant value that is derived in advance by an experiment in at least one of the first state in which the electronic device (201) is gripped by a human body or the second state in which the electronic device (201) is electrically connected to an external electronic device. According to one embodiment, the processor (220) may perform operation 1764 if the value of the first touch signal (e.g., the first value) exceeds the second threshold value. According to one embodiment, the processor (220) may perform operation 1766 if the value of the first touch signal (e.g., the first value) is equal to or less than the second threshold value.

[0200] In operation 1780, the processor (220) according to one embodiment may perform user authentication based on the determination that the fingerprint image is not forged. The processor (220) according to one embodiment may display information indicating that the fingerprint image is not forged based on the determination that the fingerprint image is not forged. The processor (220) according to one embodiment may display information indicating that user authentication is performed based on the determination that the fingerprint image is not forged.

[0201] In operation 1790, the processor (220) according to one embodiment may indicate a failure in performing user authentication based on the determination that the fingerprint image is counterfeit. The processor (220) according to one embodiment may display information indicating that the fingerprint image is counterfeit and / or information indicating a failure in performing user authentication on the display (260).

[0202] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains.

[0203] Electronic devices according to various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments of the present disclosure are not limited to the aforementioned devices.

[0204] The various embodiments of the present disclosure and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B," "at least one of A and B," "at least one of A or B," "A, B, or C," "at least one of A, B, and C," and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0205] The term "module" as used herein may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0206] Various embodiments of the present disclosure may be implemented as software (e.g., a program (140)) including one or more commands stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one command among the one or more commands stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one command called. The one or more commands may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0207] According to one embodiment, the method according to the various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0208] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0209] According to one embodiment, a non-transitory storage medium storing commands is provided, wherein the commands are configured to cause the electronic device to perform at least one operation when executed by the electronic device, wherein the at least one operation comprises: an operation of acquiring a first touch signal detected through a touch sensor (271) and a first fingerprint image detected through a fingerprint sensor (273) based on a first touch input through a display (160, 260) of the electronic device; an operation of identifying a first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device; an operation of determining that the first fingerprint image is not counterfeit if the first value of the first touch signal exceeds a first threshold value, and an operation of determining that the first fingerprint image is counterfeit if the value of the first touch signal is less than or equal to the first threshold value; and an operation of determining that the electronic device is gripped by the human body. If the grip is not engaged and the electronic device is not electrically connected to the external electronic device: If the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, the first fingerprint image is determined to be non-fake, and If the first value of the first touch signal is less than or equal to the second threshold value, the first fingerprint image is determined to be fake.

[0210] And the embodiments of the present disclosure described in this specification and drawings are merely specific examples presented to easily explain the technical contents according to the embodiments of the present disclosure and to help understand the embodiments of the present disclosure, and are not intended to limit the scope of the embodiments of the present disclosure. Therefore, the scope of the various embodiments of the present disclosure should be interpreted as including all changes or modified forms derived based on the technical ideas of the various embodiments of the present disclosure in addition to the embodiments invented herein.

Claims

1. In the electronic device (101, 201), display(160, 260); A touch sensor (271) arranged to overlap with the above display; A fingerprint sensor (273) positioned to overlap with the above display; Memory (130, 230) for storing commands; and Contains at least one processor (120, 220), The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: Based on the first touch input through the display, a first touch signal detected through the touch sensor and a first fingerprint image detected through the fingerprint sensor are acquired, Identifying the first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device; When the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device: If the first value of the first touch signal exceeds the first threshold, the first fingerprint image is determined to be not forged, and If the value of the first touch signal is less than or equal to the first threshold value, the first fingerprint image is determined to be fake; If the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device: If the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, the first fingerprint image is determined to be not forged, and An electronic device that determines that the first fingerprint image is fake if the first value of the first touch signal is less than or equal to the second threshold value.

2. In paragraph 1, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: An electronic device that identifies a value corresponding to a touch signal by a dielectric containing a fake fingerprint when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device as being smaller than a value corresponding to a touch signal by the dielectric containing the fake fingerprint when the electronic device is gripped by the human body or the electronic device is electrically connected to the external electronic device.

3. In paragraph 1 or 2, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: Obtaining a plurality of values ​​corresponding to the first touch signal using the touch map of the touch sensor, and The maximum value among the plurality of values ​​corresponding to the first touch signal is determined as the first value of the first touch signal, or An electronic device that determines the sum of the values ​​of a first portion greater than the remaining values ​​among the plurality of values ​​corresponding to the first touch signal as the first value of the first touch signal.

4. In any one of paragraphs 1 to 3, Including a gyro sensor (275), The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: If the distribution range of the values ​​sensed by the gyro sensor is narrower than the first distribution range, the electronic device identifies that the human body is not gripped, and if the distribution range of the values ​​sensed by the gyro sensor is equal to or wider than the first distribution range, the electronic device identifies that the human body is gripped, An electronic device that identifies that the electronic device is not gripped by the human body when the distribution range of the differences between the values ​​sensed at time intervals specified by the gyro sensor is narrower than the second distribution range, and identifies that the electronic device is gripped by the human body when the distribution range of the differences is equal to or wider than the second distribution range.

5. In any one of paragraphs 1 to 4, Further including an acceleration sensor (277), The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: If the distribution range of the values ​​sensed by the acceleration sensor is narrower than the third distribution range, the electronic device identifies that the human body is not gripped, and if the distribution range of the values ​​sensed by the acceleration sensor is equal to or wider than the third distribution range, the electronic device identifies that the human body is gripped, or An electronic device that identifies that the electronic device is not gripped by the human body when the distribution range of the differences between the values ​​sensed at time intervals specified by the acceleration sensor is narrower than the fourth distribution range, and identifies that the electronic device is gripped by the human body when the distribution range of the differences is equal to or wider than the fourth distribution range.

6. In any one of paragraphs 1 to 5, Including further connector (278), The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: An electronic device that identifies the electronic device as being electrically connected to the external electronic device when a cable is connected to the connector, and identifies the electronic device as not being electrically connected to the external electronic device when the cable is not connected to the connector.

7. In any one of paragraphs 1 to 6, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: An electronic device that determines whether the fingerprint image is forged using an image learning-based forged fingerprint detection model when the values ​​sensed by the gyro sensor and / or the values ​​sensed by the acceleration sensor are invalid.

8. In any one of paragraphs 1 to 7, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: Acquire a plurality of touch signals corresponding to a plurality of touch inputs of a specific user through the touch sensor while the electronic device is in at least one of the first state in which the electronic device is gripped by the human body or the second state in which the electronic device is electrically connected to the external electronic device, and An electronic device that determines the first threshold value for the specific user by multiplying the average of at least some of the values ​​of the plurality of touch signals for which fingerprint authentication is successful by a first relationship constant.

9. In any one of paragraphs 1 to 8, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: An electronic device that determines the second threshold value for the specific user by multiplying the average of at least some of the values ​​of the plurality of touch signals for which fingerprint authentication is successful by a second relationship constant that is different from the first relationship constant.

10. In any one of paragraphs 1 to 9, The above electronic device, A first classifier trained to determine whether the first fingerprint image is fake or not based on whether the first value of the first touch signal is less than or equal to the first threshold value; and A second classifier is included that is trained to determine whether the first fingerprint image is counterfeit based on whether the first value of the first touch signal is less than or equal to the second threshold value, The above instructions, when individually or collectively executed by the at least one processor, cause the electronic device to: When the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device, a first classifier is applied to the first value of the first touch signal, An electronic device that applies the second classifier to the first value of the first touch signal when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device.

11. In a method for detecting a fake fingerprint in an electronic device (101, 201), An operation of acquiring a first touch signal detected through a touch sensor (271) and a first fingerprint image detected through a fingerprint sensor (273) based on a first touch input through a display (160, 260) of the electronic device; An operation of identifying a first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device; When the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device: If the first value of the first touch signal exceeds the first threshold, the first fingerprint image is determined to be not forged, and An operation for determining that the first fingerprint image is fake if the value of the first touch signal is less than or equal to the first threshold value; and If the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device: If the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, the first fingerprint image is determined to be not forged, and A method including an operation of determining that the first fingerprint image is fake if the first value of the first touch signal is less than or equal to the second threshold value.

12. In paragraph 11, A method comprising an action of identifying a value corresponding to a touch signal by a dielectric including a fake fingerprint when the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device as being smaller than a value corresponding to a touch signal by the dielectric including the fake fingerprint when the electronic device is gripped by the human body or the electronic device is electrically connected to the external electronic device.

13. In paragraph 11 or 12, An operation of obtaining a plurality of values ​​corresponding to the first touch signal using the touch map of the touch sensor; and A method comprising an operation of determining a maximum value among the plurality of values ​​corresponding to the first touch signal as the first value of the first touch signal, or determining a sum of values ​​of a first portion greater than the remaining values ​​among the plurality of values ​​corresponding to the first touch signal as the first value of the first touch signal.

14. In any one of paragraphs 11 to 13, An operation of identifying that the electronic device is not gripped by the human body when the distribution range of values ​​sensed by the gyro sensor (275) of the electronic device is narrower than the first distribution range, and identifying that the electronic device is gripped by the human body when the distribution range of values ​​sensed by the gyro sensor is equal to or wider than the first distribution range; or A method comprising an operation of identifying that the electronic device is not gripped by the human body when the distribution range of the differences between the values ​​sensed at time intervals specified by the gyro sensor is narrower than the second distribution range, and identifying that the electronic device is gripped by the human body when the distribution range of the differences is equal to or wider than the second distribution range.

15. A non-transitory computer-readable medium having recorded thereon computer-executable instructions, wherein the computer-executable instructions, when executed, cause an electronic device including at least one processor to perform at least one operation, wherein the at least one operation is: An operation of acquiring a first touch signal detected through a touch sensor (271) and a first fingerprint image detected through a fingerprint sensor (273) based on a first touch input through a display (160, 260) of the electronic device; An operation of identifying a first value of the first touch signal, whether the electronic device is gripped by a human body, and whether the electronic device is electrically connected to an external electronic device; When the electronic device is in at least one of a first state in which the electronic device is gripped by the human body or a second state in which the electronic device is electrically connected to the external electronic device: If the first value of the first touch signal exceeds the first threshold, the first fingerprint image is determined to be not forged, and An operation for determining that the first fingerprint image is fake if the value of the first touch signal is less than or equal to the first threshold value; and If the electronic device is not gripped by the human body and the electronic device is not electrically connected to the external electronic device: If the first value of the first touch signal exceeds a second threshold value that is less than the first threshold value, the first fingerprint image is determined to be not forged, and A non-transitory computer-readable medium comprising an operation for determining that the first fingerprint image is fake if the first value of the first touch signal is less than or equal to the second threshold value.

Images