Electronic device for biometrics and method thereof

Abstract

Certain embodiments provide a biometric recognition device and method for reducing the false recognition rate of biometric information in an electronic device employing biometric recognition technology. In this process, the electronic device can store first feature values extracted from the owner's biometric information at M-ary feature points, and additionally store auxiliary biometric information from biometric information stored in one or more memories or external servers. The electronic device can perform biometric recognition on input biometric information using the first feature values and second feature values extracted from the additionally stored biometric information.

Description

TECHNICAL FIELD

[0001] One or more embodiments disclosed herein relate generally to an apparatus and method for recognizing biometric information in an electronic device that has adopted a biometric technology. BACKGROUND

[0002] A biometric technology extracts a physiological feature or a behavioral feature that can be measured from a human being, and compares and / or confirms an individual based on the extracted physiological feature or behavioral feature. That is, the biometric technology can be used to confirm an individual using one or more biometrics such as a physiological feature or a behavioral feature. The biometric information can be extracted using methods such as fingerprint recognition, iris scanning, retinal recognition, palm shape recognition, and face recognition. The biometric information based on a behavioral feature can be extracted using methods such as voice recognition or signature scanning.

[0003] For example, the biometric technology can be utilized to confirm an authorized individual or an unauthorized individual for a specific device using an individual's physiological feature or behavioral feature. In particular, the biometric technology is used in fields such as financial services, network security, or healthcare. SUMMARY

[0004] TECHNICAL PROBLEM

[0005] A specific biometric technology can provide a "search function" for finding an unspecified individual by using one item of biometric information, and a "query function" for determining whether a plurality of items of biometric information belong to the same individual. In this process, the biometric information needs to satisfy requirements such as universality (common to every individual), distinctiveness (unique per individual), permanence (does not change over time), collectability (quantitatively measurable), performance (high performance regardless of environmental changes), acceptability (individuals are willing to have their biometrics captured and evaluated), and circumvention (immune to hacking).

[0006] Table 1 shows a satisfaction level of various requirements for various types of biometric information.

[0007] Table 1

[0008]

[0009]

[0010] As shown in Table 1, biometric recognition based on facial recognition, signature recognition, and voice recognition exhibit relatively low levels of satisfaction in terms of uniqueness and circumvention. For example, facial recognition can encounter misrecognition due to individuals having similar faces, such as family members. In addition, facial recognition can be more susceptible to circumvention than other recognition techniques, such as recognition techniques using other biometric information items, because an unauthorized individual can obtain a photograph of an authorized individual's face. In addition, fingerprint recognition and voice recognition can also exhibit misrecognition due to low uniqueness.

[0011] Solutions to problems

[0012] According to an embodiment of the disclosure, an electronic device can include one or more memories, and at least one processor to access the one or more memories, wherein at least one of the one or more memories can store instructions that, when executed, cause the at least one processor to determine M-ary feature points to obtain first feature values from owner biometric information, and confirm auxiliary biometric information from biometric information stored in at least one of the one or more memories or an external server, wherein the auxiliary biometric information has similar feature values to the owner biometric information at the M-ary feature points.

[0013] According to an embodiment of the disclosure, a biometric authentication method in an electronic device can include determining M-ary feature points to obtain first feature values from owner biometric information, confirming auxiliary biometric information from biometric information stored in at least one of one or more memories or an external server, and performing biometric authentication on input biometric information based on the owner biometric information and the auxiliary biometric information, wherein the auxiliary biometric information has similar feature values to the owner biometric information at the M-ary feature points.

[0014] Other aspects, advantages, and salient features of the disclosure will become apparent to those skilled in the art from the following detailed description, taken in conduction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0015] The above and other aspects, features, and advantages of certain embodiments of the disclosure will be more apparent from the following description taken in conjunction with the accompanying drawings, in which:

[0016] Figure 1 is a block diagram of an electronic device 101 according to an embodiment of the disclosure;

[0017] Figure 2is a block diagram of a display device 160 according to an embodiment of the disclosure;

[0018] Figure 3 is a block diagram of an electronic device 101 for performing biometric recognition according to an embodiment of the disclosure;

[0019] Figure 4 is a block diagram of a biometric recognition device in an electronic device 101 according to an embodiment of the disclosure;

[0020] Figure 5 is a flowchart of operations for performing biometric authentication in an electronic device 101 according to an embodiment of the disclosure;

[0021] Figure 6 is a diagram illustrating biometric recognition in an electronic device 101 according to an embodiment of the disclosure;

[0022] Figure 7 is a diagram illustrating operations for performing an auxiliary matcher in an electronic device 101 according to an embodiment of the disclosure;

[0023] Figure 8 is a diagram of an example of operations for performing biometric recognition in an electronic device 101 according to an embodiment of the disclosure;

[0024] Figure 9 is a diagram of another example of operations for performing biometric recognition in an electronic device 101 according to an embodiment of the disclosure.

[0025] Throughout the drawings, the same reference numerals will be understood to refer to the same parts, components, and structures. DETAILED DESCRIPTION

[0026] Hereinafter, various embodiments of the disclosure will be described.

[0027] Certain embodiments of the disclosure can provide a biometric recognition apparatus and method for reducing a false recognition rate of biometric information in an electronic device capable of biometric recognition.

[0028] Certain embodiments of the disclosure can provide an apparatus and method for performing user authentication using additional biometric information that can cause false recognition in an electronic device capable of biometric recognition.

[0029] In the following description, certain embodiments provide an electronic device that performs biometric recognition by matching input biometric information with owner biometric information and auxiliary biometric information. The owner biometric information can be input by an owner, the auxiliary biometric information can have a high similarity to the owner biometric information, and the input biometric information can be input for biometric recognition.

[0030] The following description will provide: 1) embodiments for collecting auxiliary biometric information, 2) embodiments for generating an auxiliary matcher to which an auxiliary feature value extracted from the collected auxiliary biometric information is applied, and 3) embodiments for performing biometric recognition on an input feature value extracted from input biometric information using a primary matcher generated by applying a primary feature value extracted from owner biometric information and the auxiliary matcher.

[0031] According to embodiments, biometric information can be data or signals input or acquired through various devices such as a camera, a sensor, and a touch panel, or can be data or signals pre-stored in a memory (e.g., data representing biometric information such as a photo). The input, acquired, or pre-stored data or signals may, for example, be completely unprocessed raw data. The raw data may, for example, be data captured by a camera, signals sensed by a sensor, or signals output from a touch panel according to a user touch. In this case, the biometric information corresponding to the raw data can be processed through an algorithm designated by a processor of an electronic device, through normalization, or featureization.

[0032] According to embodiments, biometric information can correspond to data or signals input or acquired through various devices or pre-stored in a memory. The data or signals pre-stored in the memory can be processed through normalization or featureization of a designated algorithm and then output as biometric information. In this case, a microprocessor for normalization or featureization of data or signals corresponding to biometric information can be included in various devices such as a camera, a sensor, or a touch panel or can be implemented separately. The microprocessor can output biometric information by normalizing or featureizing input, acquired, or pre-stored raw data, for example, using a designated algorithm.

[0033] For simplicity, it is described that biometric information is output by normalizing or featureizing raw data using a designated algorithm, without designating a specific entity (e.g., a microprocessor) that performs normalization or featureization. Accordingly, embodiments of the disclosure are not limited to a specific entity (e.g., a camera, a sensor, a touch panel, or a processor of an electronic device) that performs normalization or featureization. Accordingly, various embodiments of the disclosure can be implemented regardless of an entity that performs normalization or featureization.

[0034] According to an embodiment, the biometric information can include biometric information corresponding to a behavioral feature such as a voice or a signature, and biometric information corresponding to a physiological feature such as a fingerprint, an iris, a retina, a face, or a palm shape. Hereinafter, a "biometric feature type" can be used as a technical term for distinguishing various types of biometric information such as a fingerprint, an iris, a retina, a palm shape, a face, a voice, or a signature, and a "biometric information type" can be used as a technical term for distinguishing biometric information corresponding to a biometric feature type.

[0035] According to an embodiment, the auxiliary biometric information can be collected in the following manner: 1) according to a user (owner) selection; 2) from among images including an image having an object (e.g., a fingerprint, an iris, a retina, a palm shape, a face, or a signature) having a high degree of similarity with the owner biometric information stored in an image; 3) from biometric information of a previously failed biometric recognition; or 4) from other biometric information if biometric recognition fails using first biometric information for biometric recognition among a plurality of biometric information.

[0036] According to an embodiment, the processor can use a biometric authentication database (template) that temporarily stores reference information to operate a matcher, confirm whether information extracted from a requester belongs to the owner in order to authenticate by the operated matcher, and verify that the requester is the owner based on a result. The reference information may, for example, be a first feature value extracted from the owner biometric information. The extracted information may, for example, be a second feature value extracted from biometric information input from the requester. The first feature value and the second feature value can be extracted by applying a technique of extracting a feature point from biometric information. In this case, the processor can compare the first feature value with the second feature value using the matcher, and if it is determined that the comparison result is sufficiently similar to satisfy a preset threshold value, determine that the biometric authentication requester is the owner.

[0037] According to an embodiment, the processor can operate a primary matcher using a stored primary feature value, confirm whether an input feature value extracted from input biometric information belongs to the owner through the operated primary matcher, and verify that a requester corresponding to the input biometric information is the owner based on a confirmation result. The primary feature value can be extracted from the owner biometric information and temporarily stored in a database (template).

[0038] According to an embodiment, the processor can extract auxiliary feature values from the auxiliary biometric information (which is collected by extracting the primary feature values from the owner biometric information), select feature values that are distinguished from the primary feature values from among the extracted auxiliary feature values, and operate the auxiliary matcher by applying the selected feature values.

[0039] According to an embodiment, the processor can perform biometric recognition in primary biometric recognition and secondary biometric recognition, the primary biometric recognition checking similarity between the input biometric information and the owner biometric information using the primary matcher, and the secondary biometric recognition checking similarity between the input biometric information and the auxiliary biometric information using the auxiliary matcher if the biometric recognition by the primary biometric recognition is successful. In the biometric recognition, if the biometric recognition of the primary biometric recognition is successful and the biometric recognition of the secondary biometric recognition fails, the processor can determine that the biometric recognition for the input biometric information is successful.

[0040] According to another embodiment, if the biometric recognition is successful in both the primary biometric recognition and the secondary biometric recognition, the processor can determine that the biometric recognition of the input biometric information is successful if the input biometric information is more similar to the owner biometric information than to the auxiliary biometric information.

[0041] Certain embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. FIG. 1 illustrates a network environment according to an embodiment of the present disclosure.

[0042] Figure 1 FIG. 1 illustrates an electronic device in a network environment according to an embodiment of the present disclosure.

[0043] Reference Figure 1The electronic device 101 in the network environment 100 can communicate with an electronic device 102 via a first network 198 (e.g., a short-range wireless communication network), or an electronic device 104 or a server 108 via a second network 199 (e.g., a long-range wireless communication network). The electronic device 101 can communicate with the electronic device 104 via the server 108. The electronic device 101 includes a processor 120, a memory 130, an input device 150, a sound output device 155, a display device 160, an audio module 170, a sensor module 176, an interface 177, a haptic module 179, a camera module 180, a power management module 188, a battery 189, a communication module 190, a subscriber identification module (SIM) 196, and an antenna module 197. Alternatively, at least one of the components (e.g., the display device 160 or the camera module 180) can be omitted from the electronic device 101, or one or more other components can be added in the electronic device 101. Some of the components can be implemented as single integrated circuitry. For example, the sensor module 176 (e.g., a fingerprint sensor, an iris sensor, or an illuminance sensor) can be implemented as embedded in the display device 160 (e.g., a display).

[0044] The processor 120 can execute, for example, software (e.g., a program 140) to control at least one other component (e.g., a hardware or software component) of the electronic device 101 coupled with the processor 120, and can perform various data processing or computation. As at least part of the data processing or computation, the processor 120 can load a command or data received from another component (e.g., the sensor module 176 or the communication module 190) in volatile memory 132, process the command or data stored in the volatile memory 132, and store resulting data in non-volatile memory 134. The processor 120 includes a main processor 121 (e.g., a central processing unit (CPU) or an application processor (AP)), and an auxiliary processor 123 (e.g., a graphics processing unit (GPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is operable independently from, or in conjunction with, the main processor 121. Additionally, or alternatively, the auxiliary processor 123 can be adapted to consume less power than the main processor 121, or to operate a specific function or functions. The auxiliary processor 123 can be implemented as separate from or as part of the main processor 121.

[0045] The auxiliary processor 123 can control at least some of functions or states related to at least one component (e.g., the display device 160, the sensor module 176, or the communication module 190) among the components of the electronic device 101, instead of the main processor 121, when the main processor 121 is in an inactive (e.g., sleep) state, or together with the main processor 121, when the main processor 121 is in an active state (e.g., executing an application), or some of the functions or states related to at least one component (e.g., the display device 160, the sensor module 176, or the communication module 190) among the components of the electronic device 101. The auxiliary processor 123 (e.g., an ISP or a CP) can be implemented as a part of another component (e.g., the camera module 180 or the communication module 190) functionally related to the auxiliary processor 123.

[0046] The memory 130 can store various data used by at least one component (e.g., the processor 120 or the sensor module 176) of the electronic device 101. The various data can include software (e.g., the program 140) and input data or output data for commands related thereto. The memory 130 includes the volatile memory 132 and the non-volatile memory 134.

[0047] The program 140 can be stored in the memory 130 as software, and can include, for example, an operating system (OS) 142, middleware 144, or an application 146.

[0048] The input device 150 can receive a command or data, which is used for another component (e.g., the processor 120) of the electronic device 101, from the outside (e.g., a user) of the electronic device 101. The input device 150 can include, for example, a microphone, a mouse, a keyboard, and / or a digital pen (e.g., a stylus pen).

[0049] The sound output device 155 can output sound signals to the outside of the electronic device 101. The sound output device 155 can include a speaker or a receiver. The speaker can be used for general purposes, such as playing multimedia or playing record, and the receiver can be used for incoming calls. The receiver can be implemented as separate from the speaker, or can be implemented as a part of the speaker.

[0050] The display device 160 can visually provide information to the outside (e.g., a user) of the electronic device 101. The display device 160 can include a display, a hologram device, or a projector, and a control circuit for controlling a corresponding one of the display, the hologram device, and the projector. The display device 160 can include a touch circuit adapted to detect a touch, or a sensor circuit (e.g., a pressure sensor) adapted to measure the intensity of force incurred by the touch.

[0051] The audio module 170 can convert a sound into an electrical signal and vice versa. The audio module 170 can obtain the sound from a microphone of the input device 150 or output the sound through a speaker of the sound output device 155 or a headphone of an external electronic device (e.g., an electronic device 102) directly (e.g., wiredly) or wirelessly coupled with the electronic device 101.

[0052] The sensor module 176 can detect an operational state (e.g., power or temperature) of the electronic device 101 or an environmental state (e.g., a state of a user) external to the electronic device 101, and then generate an electrical signal or data value corresponding to the detected state. The sensor module 176 can include a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0053] The interface 177 can support one or more specified protocols to be used for the electronic device 101 to be coupled with the external electronic device (e.g., the electronic device 102) directly (e.g., wiredly) or wirelessly. The interface 177 can include a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, a secure digital (SD) card interface, or an audio interface.

[0054] The connection terminal 178 can include a connector through which the electronic device 101 can be physically connected with the external electronic device (e.g., the electronic device 102). The connection terminal 178 can include a HDMI connector, a USB connector, a SD card connector, or an audio connector (e.g., a headphone connector).

[0055] The haptic module 179 can convert an electrical signal into a mechanical stimulus (e.g., a vibration or movement) or an electrical stimulus that can be recognized by a user via his tactile sensation or kinesthetic sensation. The haptic module 179 can include a motor, a piezoelectric element, or an electrical stimulation device.

[0056] The camera module 180 can capture a still image or moving images. The camera module 180 can include one or more lenses, image sensors, ISPs, or flashes.

[0057] The power management module 188 can manage power supplied to the electronic device 101. The power management module 188 can be implemented as at least part of a power management IC (PMIC).

[0058] The battery 189 can supply power to at least one component of the electronic device 101. The battery 189 can include a primary cell which is not rechargeable, a secondary cell which is rechargeable, and / or a fuel cell.

[0059] The communication module 190 can support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (e.g., the electronic device 102, the electronic device 104, or the server 108) and performing communication between the established communication channel. The communication module 190 can include one or more CPs that can operate independently of the processor 120 (e.g., an AP) and support direct (e.g., wired) communication or wireless communication. The communication module 190 can 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 (PLC) module). A corresponding one of these communication modules can communicate with an external electronic device, via a first network 198 (e.g., a short-range communication network, such as Bluetooth, wireless-fidelity (Wi-Fi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., a long-range communication network, such as a cellular network, the Internet, or a computer network (e.g., LAN or wide area network (WAN)). These various types of communication modules can be implemented as a single component (e.g., a single chip), or can be implemented as separate components (e.g., separate chips) from each other. The wireless communication module 192 can identify and authenticate the electronic device 101 in a communication network, such as the first network 198 or the second network 199, using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the SIM 196.

[0060] The antenna module 197 can transmit or receive a signal or power to or from an external electronic device (e.g., an external electronic device) of the electronic device 101. According to an embodiment, the antenna module 197 can include an antenna including a radiating element composed of a conductive material or a conductive pattern formed in or on a substrate (e.g., a printed circuit board (PCB)). The antenna module 197 can include a plurality of antennas. In this case, at least one antenna appropriate for a communication scheme used in a communication network, such as the first network 198 or the second network 199, can be selected from the plurality of antennas by, for example, the communication module 190 (e.g., the wireless communication module 192). Then, the signal or the power can be transmitted or received between the communication module 190 and an external electronic device via the selected at least one antenna. An additional component (e.g., a radio frequency IC (RFIC)) other than the radiating element can be additionally formed as part of the antenna module 197.

[0061] At least some of the above-described components can be coupled to each other via an inter-peripheral communication scheme (e.g., a bus, general purpose input output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)), and communicate signals (e.g., commands or data) between them.

[0062] Commands or data can be transmitted or received between the electronic device 101 and the external electronic device 104 via the server 108 connected with the second network 199. Each of the electronic devices 102 and 104 can be a device of the same type as, or a different type from, the electronic device 101. All or some of the operations to be executed at the electronic device 101 can be executed at one or more of the external electronic devices 102, 104, or 108. For example, if the electronic device 101 is to automatically perform a function or service or is to perform a function or service in response to a request from a user or another device, the electronic device 101, instead of executing the function or service, or in addition to executing the function or service, can request at least some of the function or service to be executed by the one or more external electronic devices. The one or more external electronic devices that receive the request can execute at least some of the requested function or service, or perform another function or service related to the request, and transfer a result of the execution to the electronic device 101. The electronic device 101 can provide the result, with or without further processing of the result, as a part of a reply corresponding to the request. To this end, a cloud computing technique, a distributed computing technique, or a client-server computing technique can be used, for example.

[0063] The electronic device can be one of various types of electronic devices. The electronic devices can include, for example, a portable communication device (e.g., a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance. However, the electronic devices are not limited to the above-listed examples.

[0064] It should be understood that certain embodiments of the disclosure and the like used therein are not intended to limit the technical features set forth herein to specific embodiments, and include various changes, equivalents, or alternatives to corresponding embodiments. For the description of the drawings, like reference numerals can be used to refer to like or related elements. It will be understood that singular forms of a noun including the terms "a", "an", and "the" can include one or more things unless the relevant context clearly indicates otherwise.

[0065] As used herein, each of the phrases such as "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 or all possible combinations of the items listed together in the corresponding one of the phrases. As used herein, terms such as "1st" and "2nd" or "first" and "second" can be used to simply distinguish a corresponding component from another, and do not in other manners (e.g., importance or order) limit the corresponding component. It will be understood that, if an element (for example, first element) is referred to as being "operatively or communicatively connected" with another element (for example, second element), or if it is not used that the term "operatively or communicatively connected" is used, it means that the one element can be connected with the other element directly (for example, wiredly), wirelessly, or via a third element.

[0066] As used herein, the term "module" can include a unit implemented in hardware, software, or firmware, and can interchangeably be used with other terms, for example, "logic," "logic block," "part," or "circuitry." The module can be a single integral component, or a minimum unit or part thereof, adapted to perform one or more functions. For example, according to an embodiment, the module can be implemented in a form of an application-specific IC (ASIC).

[0067] Certain embodiments set forth herein can be implemented as software (e.g., the program 140) including one or more instructions that are stored in a storage medium (e.g., the internal memory 136 or the external memory 138) that are readable by a machine (e.g., the electronic device 101). For example, a processor (e.g., the processor 120) of the machine (e.g., the electronic device 101) can invoke at least one of the one or more instructions stored in the storage medium, and execute it, with or without using one or more other components under the control of the processor. This allows the machine to be operated to perform at least one function according to the at least one instruction invoked.

[0068] The one or more instructions can include code produced by a compiler or code that can be executed by an interpreter. The machine-readable storage medium can be provided in the form of a non-transitory storage medium. Therein, the term "non-transitory" only means that the storage medium is a tangible device, and does not include a signal (for example, an electromagnetic wave), but this term does not differentiate between where data is semi-permanently stored in the storage medium and where the data is temporarily stored in the storage medium.

[0069] The method according to an embodiment of the disclosure can be included and provided in a computer program product. The computer program product can be traded as a product between a seller and a buyer. The computer program product can be distributed in the form of a machine-readable storage medium (e.g., compact disc read only memory (CD-ROM)), or be distributed online via an application store (e.g., PlayStore TM ). If the computer program product is distributed online, at least part of the computer program product can be temporarily generated in a storage medium of the manufacturer, the application store, or a server, etc. The computer program product includes a program that is executable by one or more electronic devices such as, for example, a personal computer (PC), a smartphone, or a tablet PC, and includes a command that is executable by one or more electronic devices. The program can be written as code, a plug-in, a firmware, or a microcode. At least some of the one or more electronic devices can be a server, a computer, a personal computer (PC), a laptop computer, a notebook computer, a subnotebook computer, a smart phone, a smart pad, a personal digital

[0070] Each of the above-described components (e.g., a module or a program) can include a single entity or multiple entities. One or more of the above-described components can be omitted, or one or more other components can be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) can be integrated into a single component. In such a case, the integrated component can still perform one or more functions of the plurality of components in the same or similar manner as they are performed by the plurality of components before the integration. Operations performed by the module, the program, or another component can be executed sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations can be executed in a different order or omitted, or one or more other operations can be added.

[0071] According to certain embodiments, the processor 120 can establish and manage a communication session between the electronic device 101 and a network, and can control a function for maintaining a persistent communication based on movement of the electronic device 101. According to an embodiment, the processor 120 can acquire a tracking area identifier (TAI) list during an attach procedure or a tracking area update (TAU) procedure. The TAI list can include identifiers of at least one tracking area managed by a corresponding mobility management entity (MME). For example, when the tracking areas managed by the MME are a first tracking area and a second tracking area, the TAI list can include an identifier of the first tracking area and an identifier of the second tracking area. The identifier of the tracking area can consist of a public land mobile network identifier (PLMN ID) and / or a tracking area code (TAC). For example, the processor 120 can transmit an attach request message for network attachment through the communication module 190, and can receive an attach accept message including the TAI list in response to the attach request message transmission. For another example, when there is a change in a tracking area in which the electronic device 101 is located or there is a change in a radio access technology (RAT), the processor 120 can transmit a TAU request message, and in response to the TAU request message, can receive a TAU accept message including the TAI list. According to an embodiment, the processor 120 can store the acquired TAI list in the memory 130.

[0072] According to certain embodiments, the processor 120 can transmit an attach request message and / or a TAU request message, and can determine whether an attach failure and / or a TAU failure occurs. For example, when the attach or the TAU is rejected due to a weak field situation or an unspecified reason, the processor 120 can identify occurrence of an attach failure caused by a lower layer failure or a TAU failure caused by a lower layer failure. For another example, when a response message for the attach request is not received within a first duration specified from timing at which the attach request message is transmitted, the processor 120 can determine that the attach has failed. The response message for the attach request can include at least one of an attach accept message and an attach reject message. The specified first duration may, for example, be a length of time for which a T3410 timer is running. For another example, when a response message for the TAU request is not received within a second duration specified from timing at which the TAU request message is transmitted, the processor 120 can identify that the TAU has failed. The response message for the TAU request can include at least one of a TAU accept message and a TAU reject message. The specified second duration may, for example, be a length of time for which a T3430 timer is running.

[0073] According to certain embodiments, upon occurrence of the attach failure and / or the TAU failure, the processor 120 can run a designated first timer (e.g., T3411). The first timer can be a timer for measuring a wait timer for retransmission of the attach request message or retransmission of the TAU request message. For example, when the first timer expires, the processor 120 can retransmit the attach request message or the TAU request message. According to an embodiment, the processor 120 can perform cell reselection during the running of the first timer. For example, the processor 120 can perform cell reselection that moves away from the first cell in which the attach or the TAU is attempted, to a second cell that is adjacent. In this document, the cell reselection can include not only a cell reselection operation of the electronic device 101 in an idle state after network attachment, but also an operation in which the electronic device 101 that has lost network connection camps on another cell.

[0074] According to an embodiment, when the channel quality of the second cell is better than that of the first cell, the processor 120 can allow cell reselection to the second cell through the communication module 190. According to an embodiment, regardless of the channel quality of the first cell and the channel quality of the second cell, the processor 120 can forcibly prohibit use of the first cell and can allow cell reselection to the second cell through the communication module 190. For example, even if the channel quality of the first cell is better than that of the second cell, the processor 120 can control the communication module 190 to perform cell reselection to the second cell that is relatively more adjacent than the first cell. According to an embodiment, use of the first cell can be forcibly prohibited only during the running of the first timer, and when the first timer expires, the prohibition of the first cell can be released, thereby returning to the first cell based on the channel quality.

[0075] According to certain embodiments, the processor 120 can determine whether the tracking area of the reselected second cell is included in the TAI list stored in the memory 130. For example, the processor 120 can compare the TAI of the reselected second cell with the TAIs included in the TAI list stored in the memory 130 to determine whether the tracking area of the reselected second cell is included in the TAI list. According to an embodiment, if the tracking area of the reselected second cell is not included in the TAI list stored in the memory 130, the processor 120 can forcibly terminate the first timer, and can transmit an attach request message or a TAU request message to the base station of the second cell. According to an embodiment, if the tracking area of the reselected second cell is included in the TAI list stored in the memory 130, the processor 120 can determine whether the first timer is forcibly terminated based on the channel quality of the reselected second cell. For example, if the received signal strength of the reselected second cell is greater than a threshold value, the processor 120 can forcibly terminate the first timer, and can transmit an attach request message or a TAU request message to the base station of the reselected second cell. For another example, if the received signal strength of the reselected second cell is less than or equal to the threshold value, the processor 120 can wait until the first timer is terminated, and after the first timer is terminated, can transmit an attach request message or a TAU request message to the base station of the reselected second cell. According to an embodiment, if the tracking area of the reselected second cell is included in the TAI list stored in the memory 130, the processor 120 can forcibly terminate the first timer regardless of the channel quality of the reselected second cell, and can retransmit an attach request message or a TAU request message. According to an embodiment, if the tracking area of the reselected second cell is not included in the TAI list stored in the memory 130, the processor 120 can transmit a service request message to the base station of the reselected second cell. For example, if the TAI of the reselected cell is included in the TAI list stored in the memory 130 in a situation in which the first timer is running due to TAU failure, the processor 120 can determine that there is no need to perform a TAU procedure. If it is determined that there is no need to perform a TAU procedure, the processor 120 can omit the operation of transmitting a TAU request message, and can transmit a service request message.

[0076] Figure 2 FIG. 2 is a block diagram 200 illustrating a display device 160 according to an embodiment.

[0077] Reference Figure 2The display device 160 can include a display 210 and a display driver integrated circuit (DDI) 230 for controlling the display 210. The DDI 230 can include an interface module 231, a memory 233 (e.g., a buffer memory), an image processing module 235, or a mapping module 237. The DDI 230 can receive image information including image data or an image control signal corresponding to a command for controlling the image data from another component of the electronic device 101 via the interface module 231. For example, according to an embodiment, the image information can be received from the processor 120 (e.g., a main processor 121 (e.g., an application processor)) or an auxiliary processor 123 (e.g., a graphic processing unit) operating independently of a function of the main processor 121. The DDI 230 can communicate with, for example, the input device 150 (e.g., a touch circuit) or the sensor module 176 via the interface module 231. The DDI 230 can also store at least a portion of the received image information in the memory 233, for example, frame by frame.

[0078] The image processing module 235 can perform pre-processing or post-processing (e.g., adjustment of resolution, brightness, or size) with respect to at least a portion of the image data. According to an embodiment, for example, the pre-processing or post-processing can be performed based at least in part on one or more characteristics of the image data or one or more characteristics of the display 210.

[0079] The mapping module 237 can generate voltage values or current values corresponding to the image data pre-processed or post-processed by the image processing module 235. According to an embodiment, for example, the generation of the voltage values or current values can be performed based at least in part on one or more attributes of the pixels (e.g., an array of the pixels such as RGB stripes or a pentile structure or a size of each sub-pixel). For example, at least some of the pixels of the display 210 can be driven based at least in part on the voltage values or current values, so that visual information (e.g., text, an image, or an icon) corresponding to the image data can be displayed via the display 210.

[0080] According to an embodiment, the display device 160 can further include a touch circuit 250. The touch circuit 250 can include a touch sensor 251 and a touch sensor IC 253 for controlling the touch sensor 251. The touch sensor IC 253 can control the touch sensor 251 to sense a touch input or a hovering input for a specific location on the display 210. To this end, for example, the touch sensor 251 can detect (e.g., measure) a change in a signal (e.g., a voltage, an amount of light, a resistance, or one or more amounts of electric charges) corresponding to a specific location on the display 210. The touch circuit 250 can provide input information (e.g., a location, an area, a pressure, or a time) indicating the touch input or the hovering input detected via the touch sensor 251 to the processor 120. According to an embodiment, at least a part (e.g., the touch sensor IC 253) of the touch circuit 250 can be formed as a part of the display 210 or the DDI 230, or as a part of another component (e.g., the auxiliary processor 123) disposed outside the display device 160.

[0081] According to an embodiment, the display device 160 can further include at least one sensor (e.g., a fingerprint sensor, an iris sensor, a pressure sensor, or an illuminance sensor) of the sensor module 176 or a control circuit for the at least one sensor. In this case, the at least one sensor or the control circuit for the at least one sensor can be embedded in a part of a component (e.g., the display 210, the DDI 230, or the touch circuit 150) of the display device 160. For example, when the sensor module 176 embedded in the display device 160 includes a biometric sensor (e.g., a fingerprint sensor), the biometric sensor can acquire biometric information (e.g., a fingerprint image) corresponding to a touch input received via a part of the display 210. As another example, when the sensor module 176 embedded in the display device 160 includes a pressure sensor, the pressure sensor can acquire pressure information corresponding to a touch input received via a partial area or the entire area of the display 210. According to an embodiment, the touch sensor 251 or the sensor module 176 can be disposed between pixels in a pixel layer of the display 210, or above or below the pixel layer.

[0082] Figure 3 is an electronic device (e.g., the electronic device 101 of FIG. 1) for performing biometric authentication according to an embodiment of the disclosure. Figure 1 is a block diagram 300 of an electronic device 101 according to an embodiment of the disclosure.

[0083] Referring to Figure 3 , the electronic device 101 according to an embodiment can include a biometric recognition module 320, a processor 310 (e.g., the processor 120 of FIG. 1), or a memory 330 (e.g., the memory 130 of FIG. 1). Figure 1 Figure 1 ​The electronic device 101 can acquire biometric information from at least one of one or more biometric types (e.g., fingerprint, iris, retina, palm print, face, voice, signature), and can compare the feature values ​​extracted from the acquired biometric information with those obtained by the owner (e.g., [the owner]). Figure 1 The owner of the electronic device 101 identifies the owner's biometric information by matching the feature values ​​of pre-stored biometric information. Biometric information may be, for example, raw data acquired by the biometric identification module 320 or data processed by normalizing or characterizing the raw data. In this case, the biometric identification module 320 may include components such as a microprocessor for normalizing or characterizing the raw data. If the biometric identification module 320 does not support raw data processing, it may provide the raw data to the processor 310. In this case, the processor 310 may normalize or characterize the raw data fed from the biometric identification module 320. Hereinafter, for the sake of simplicity, it is assumed that the biometric identification module 320 outputs biometric information by normalizing or characterizing the raw data. However, one or more embodiments of this disclosure are not limited to the case where the biometric identification module 320 normalizes or characterizes the raw data.

[0084] According to an embodiment, the biometric identification module 320 can obtain at least one biometric information identified from one or more biometric types (e.g., fingerprint, iris, retina, palm print, face, voice, signature), and provide the obtained at least one biometric information to the processor 310. The biometric identification module 320 may, for example, include an input device 321 (e.g., ...). Figure 1 Input device 150), sensor module 323 (e.g., Figure 1 or Figure 2 The sensor module) or camera module 325 (e.g., Figure 1The processor 310 can include a microprocessor or any suitable type of processing circuitry such as one or more general-purpose processors (e.g., ARM-based processors), a Digital Signal Processor (DSP), a Programmable Logic Device (PLD), an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a Graphics Processing Unit (GPU), a video card controller, and the like. Moreover, it will be recognized that when a general purpose computer accesses code for implementing the processing illustrated in the figures, the execution of the code transforms the general purpose computer into a special purpose computer for executing the processing illustrated in the figures. Any of the functions and steps provided in the figures can be implemented in hardware, software, or a combination of the two, and can be performed in whole or in part within programmed instructions of a computer. No claim element in the claims is to be construed under the provisions of 35 U.S.C. 112(f) unless the word "means" is specifically used. Additionally, the term "processor" or "microprocessor" can be hardware in the claimed disclosure. The appended claims are to be construed in accordance with the broadest interpretation under the doctrine of equivalents under 35 U.S.C. § 101.

[0085] According to an embodiment, the input device 321 can receive biometric information (e.g., voice or signature) corresponding to behavioral characteristics from the owner, the authentication target, or the third party, and provide the input biometric information to the processor 310. The biometric information corresponding to the voice or signature input from the owner can be the owner biometric information. The biometric information corresponding to the voice or signature input from the authentication target can be the input biometric information. The biometric information corresponding to the voice or signature input from the third party can be the auxiliary biometric information.

[0086] According to an embodiment, the sensor module 323 can identify the physiological characteristics (e.g., fingerprint, iris, or palm shape) of the owner, the authentication target, or the third party, and provide biometric information corresponding to the identified physiological characteristics to the processor 310. The biometric information corresponding to the fingerprint, iris, or palm shape input from the owner can be the owner biometric information. The biometric information corresponding to the fingerprint, iris, or palm shape input from the authentication target can be the input biometric information. The biometric information corresponding to the fingerprint, iris, or palm shape input from the third party can be the auxiliary biometric information.

[0087] According to an embodiment, the camera module 325 can capture a face of an owner, an authentication target, or a third party, and provide the processor 310 with biometric information corresponding to the captured face. The biometric information corresponding to the captured face of the owner can be the owner biometric information. The biometric information corresponding to the captured face of the authentication target can be the input biometric information. The biometric information corresponding to the captured face of the third party can be the auxiliary biometric information.

[0088] According to an embodiment, the input device 321, the sensor module 323, or the camera module 325 of the electronic device 101 can be included in the biometric recognition module 320. The biometric recognition module 320 can be activated to acquire the owner biometric information from the owner who initially sets a biometric recognition function. The biometric recognition module 320 can also be activated to acquire the input biometric information from the authentication target in response to a biometric authentication request, or the auxiliary biometric information from a third party or a stored image. The input device 321, the sensor module 323, or the camera module 325 of the biometric recognition module 320 can include, for example, a microprocessor (e.g., a sensor hub) for converting a biometric image into a digital form and generating biometric information using the converted digital signal. The microprocessor can thus be included in, for example, the biometric recognition module 320.

[0089] According to an embodiment, the processor 310 can apply a primary feature value extracted from the owner biometric information to a primary matcher (or a first matcher), and apply an auxiliary feature value extracted from the auxiliary biometric information to an auxiliary matcher (or a second matcher). For example, applying the extracted feature value to the matcher can include executing the matcher at the processor 310 using the extracted value. The processor 310 can collect the auxiliary biometric information from, for example, biometric information such as a pre-stored photo, biometric information selected by a user, or input biometric information. The processor 310 can perform a primary biometric authentication on the input biometric information through the primary matcher, and perform a secondary biometric authentication on the input biometric information through the auxiliary matcher after considering a result of the primary biometric authentication. If the primary biometric authentication succeeds and the secondary biometric authentication fails, the processor 310 can determine that the input biometric information matches the owner biometric information.

[0090] According to an embodiment, the processor 310 can perform biometric recognition by storing or matching one or more biometric information provided from the biometric recognition module 320 for biometric recognition. The processor 310 can perform biometric recognition in the order of, for example, "acquisition → feature extraction → comparison → similarity determination." During acquisition, the processor 310 can acquire biometric information by converting a biometric feature of a biometric image into a digital form. During feature extraction, the processor 310 can extract a feature value of a feature point that is unique and highly discriminative per individual from the biometric information. During comparison, the processor 310 can compare a stored feature value with an input feature value. During similarity determination, the processor 310 can determine an individual based on a comparison result and / or based on a discriminant for minimizing false recognition. The stored feature value can be at least temporarily stored in, for example, the memory 330. The feature value temporarily stored in the memory can be updated by, for example, an event such as a user request.

[0091] According to an embodiment, the processor 310 can apply different feature values stored in the memory to at least two matchers, perform matching using the at least two matchers, and thus determine whether input biometric information is authenticated. The processor 310 can use a biometric authentication database (template) that temporarily stores reference information for each matcher to confirm whether information extracted from a requester for authentication belongs to an owner and thus verify that the requester is the owner.

[0092] According to an embodiment, the processor 310 can apply a primary feature value extracted from owner biometric information to a primary matcher (e.g., a first matcher) and apply a secondary feature value extracted from secondary biometric information highly similar to the owner biometric information to a secondary matcher (e.g., a second matcher).

[0093] The auxiliary biometric information is highly likely to be misrecognized as the owner biometric information. For example, if the owner biometric information is acquired through face recognition, the auxiliary biometric information can be biometric information extracted from a photo of a face of a family member or a relative. The auxiliary biometric information can be: extracted from a bioimage selected by a user, determined to be highly similar to the owner biometric information using a primary matcher, or selected from biometric information of a number of consecutive biometric recognition failures (e.g., 5 times). For example, in multi-modal biometric authentication, if biometric authentication using biometric information of a first type fails, biometric information of a second type can be selected as auxiliary biometric information. The auxiliary biometric information can be selected by, for example, the primary matcher. If feature values extracted from biometric information corresponding to at least one face in a stored photo match the primary feature values applied to the primary matcher and it is confirmed that similarity beyond a certain level is obtained, the processor 310 can select the corresponding biometric information as auxiliary biometric information.

[0094] According to an embodiment, the primary feature values can determine a number of feature points that are easily distinguished from other feature points in the owner biometric information, and are extracted through values representing features of the determined feature points. The auxiliary feature values can determine a number of feature points that are easily distinguished from other feature points in the auxiliary biometric information including the owner, and are extracted through values representing features of the determined feature points. The types of the feature points and the feature values can differ according to, for example, the type of the biometric information. The feature points used to extract the primary feature values and the auxiliary feature values can be the same or can overlap each other. For example, the feature points used to extract the primary feature values can include the feature points used to extract the auxiliary feature values.

[0095] According to an embodiment, the primary feature values generated in an initial setting for biometric authentication can be applied to the primary matcher. The auxiliary feature values can be applied or updated to the auxiliary matcher, for example, each time the auxiliary biometric information is acquired. The processor 310 can apply the same or similar conditions to extract the primary feature values and the auxiliary feature values. For example, the processor 310 can apply the same or similar conditions to extract one feature value. The processor 310 can not apply all of the auxiliary feature values extracted from the auxiliary biometric information to the auxiliary matcher, but can select and apply some of the extracted auxiliary feature values that are easily distinguished from the primary feature values.

[0096] According to an embodiment, if biometric recognition of the input biometric information is requested, the processor 310 can extract input feature values from the input biometric information and match the input feature values with primary feature values using a primary matcher. If a matching result of the primary matcher satisfies a preset critical condition, the processor 310 can identify that the input biometric information of the owner is identified. This can be referred to as primary biometric recognition or main biometric recognition. If the input biometric information of the owner is identified in the primary biometric recognition, the processor 310 can match some or all of the input feature values with secondary feature values applied to a secondary matcher through a secondary matcher. If a matching result of the secondary matcher does not satisfy a preset critical condition, the processor 310 can finally determine that the input biometric information of the owner is identified. This can be referred to as secondary biometric recognition or auxiliary biometric recognition.

[0097] For example, the processor 310 can perform biometric recognition in the order of "input biometric information acquisition → feature value extraction → comparison of input feature values and primary feature values → primary similarity determination → comparison of input feature values and secondary feature values → secondary similarity determination."

[0098] According to an embodiment, if the input biometric information and the owner biometric information are determined to belong to the same individual because they are sufficiently similar, and the input biometric information and the secondary biometric information (e.g., biometric information similar to the owner biometric information) are not determined to belong to the same individual because they are not sufficiently similar, the processor 310 can identify that the input biometric information of the owner is identified.

[0099] According to an embodiment, if the input biometric information of the owner is identified in the primary biometric recognition, the processor 310 can match some or all of the input feature values extracted from the input biometric information with secondary feature values using a secondary matcher. If a matching result of the secondary matcher satisfies a preset critical condition, the processor 310 can compare a first similarity (e.g., a primary similarity) with a second similarity (e.g., a secondary similarity) and finally determine whether the input biometric information of the owner is based on a comparison result. The first similarity can indicate a similarity between the input feature values and the owner feature values, and the second similarity can indicate a similarity between the input feature values and the secondary feature values. If the first similarity is higher than the second similarity, the processor 310 can finally determine the input biometric information of the owner.

[0100] According to an embodiment, if the input biometric information and the owner's biometric information are sufficiently similar to determine that they belong to the same individual, the input biometric information and the auxiliary biometric information are also sufficiently similar to determine that they belong to the same individual, and the input biometric information is more similar to the owner's biometric information than the auxiliary biometric information, then the processor 310 can determine the owner's input biometric information.

[0101] According to an embodiment, memory 330 may store instructions for execution by processor 310 to perform biometric identification. Memory 330 may, for example, store one or more owner biometric information, one or more auxiliary biometric information, primary feature values ​​extracted from one or more owner biometric information, and auxiliary feature values ​​extracted from one or more auxiliary biometric information. Memory 330 may, for example, include an image database storing photographs used to acquire auxiliary biometric information.

[0102] Figure 4 Electronic devices for biometric identification according to embodiments of the present disclosure (e.g., Figure 1 Block diagram of electronic device 101. Figure 4 The components in the electronic device 101 may be made by a processor (e.g., Figure 3 The processor 310) is stored in memory (e.g., Figure 3 A set of one or more instructions in the memory 330. For example, the fetch module 410 or the matching module 420 may correspond to a module implemented by the processor 310 by executing at least a portion of the instructions. Hereinafter, for ease of explanation, Figure 4 The embodiments are implemented in software running by the processor 310 of the electronic device 101. However, this disclosure is not so limited. Figure 4 Examples of this can be implemented in hardware.

[0103] refer to Figure 4According to an embodiment, the electronic device 101 can include an extraction module 410 and / or a matching module 420. The matching module 420 can include, for example, a first matcher 421 (e.g., a primary matcher) and / or a second matcher 423 (e.g., a secondary matcher). The first matcher 421 and / or the second matcher 423 can be provided with feature values obtained by the matching module 420 from biometric information for biometric recognition, and then stored in the memory 330. For example, information (e.g., primary feature values, secondary feature values) to be stored in the memory can be newly provided to the first matcher 421 and / or the second matcher 423 of the matching module 420, or the existing information of the matchers can be updated with the information (e.g., primary feature values, secondary feature values) to be stored in the memory.

[0104] According to an embodiment, the extraction module 410 can extract feature values at a plurality of preset feature points from biometric information of at least one of one or more biometric types (e.g., a fingerprint, an iris, a retina, a palm shape, a face, a voice, and a signature). The feature points can be determined, set, or stored by considering, for example, a type of the biometric information or a feature for easily identifying target biometric information from other biometric information. The biometric information can be, for example, one of owner biometric information, secondary biometric information, or input biometric information. The owner biometric information can be provided from an owner during initialization for biometric authentication. The secondary biometric information can be biometric information having a high similarity to the owner biometric information. The secondary biometric information can be collected from input or stored biometric information. The input biometric information can be input in a request for biometric recognition. The owner biometric information, the secondary biometric information, or the input biometric information has been described earlier, and detailed descriptions thereof should not be repeated.

[0105] According to an embodiment, the extraction module 410 can determine, for example, a plurality of primary feature points for distinguishing an owner from others in the owner biometric information, and extract primary feature values of features of the determined primary feature points from the owner biometric information. In addition, the extraction module 410 can determine, for example, a plurality of secondary feature points for distinguishing a target person from others including the owner in the secondary biometric information, and extract secondary feature values of features of the determined secondary feature points from the secondary biometric information. The extraction module 410 can extract, for example, input feature values from among the plurality of primary feature points or the plurality of secondary feature points in the input biometric information.

[0106] According to an embodiment, the types of the feature points and the feature values can be different according to the type of the biometric information. The feature points used to extract the primary feature values and the secondary feature values can be the same or can overlap. For example, the feature points used to extract the primary feature values can include the feature points used to extract the secondary feature values. The primary feature values and the secondary feature values can be extracted by applying the same or similar conditions to the extraction module 410 (e.g., using one feature value extractor).

[0107] According to an embodiment, the matching module 420 can apply the primary feature values extracted from the extraction module 410 to the first matcher 421. For example, the feature values generated in an initial setting for biometric authentication can be applied to the first matcher 421. The first matcher 421 can compare the input feature values extracted in the extraction module 410 with the primary feature values and output a biometric recognition result by determining whether the input biometric information is the owner biometric information based on a comparison result. For example, if the input feature values match the primary feature values beyond a preset threshold, the first matcher 421 can recognize that the input biometric information is the owner biometric information.

[0108] According to an embodiment, the matching module 420 can apply the secondary feature values extracted in the extraction module 410 to the second matcher 423. The secondary matcher 423 can be newly provided with the secondary feature values or updated with new secondary feature values each time the secondary biometric information is acquired, for example. Not all of the secondary feature values extracted by the extraction module 410 can be applied to the second matcher 423, but some of the extracted secondary feature values that are easily distinguished from the primary feature values can be selected and applied. If the first matcher 421 recognizes that the input biometric information is the owner biometric information, the second matcher 423 can compare the input feature values extracted in the extraction module 410 with the secondary feature values and re-recognize whether the input biometric information is the owner biometric information based on a comparison result. For example, if the input feature values do not match the secondary feature values beyond a preset threshold, the second matcher 423 can recognize that the input biometric information is the owner biometric information.

[0109] According to an embodiment, the matching module 420 can perform a primary matching between the input feature values and the main feature values through the first matcher 421, and determine whether a first matching result satisfies a preset threshold condition. If the owner's input biometric information is determined, the matching module 420 can perform a secondary matching between some or all of the input feature values and the auxiliary feature values through the second matcher 423. If a matching result of the second matcher 423 does not satisfy the preset threshold condition, the matching module 420 can finally determine the owner's input biometric information. This can be referred to as a secondary biometric recognition or an auxiliary biometric recognition.

[0110] For example, Figure 4 The biometric recognition module 400 of FIG. 4 can perform biometric recognition in a process of "input biometric information acquisition → feature value extraction → comparison of input feature values and main feature values → primary similarity determination → comparison of input feature values and auxiliary feature values → secondary similarity determination."

[0111] According to an embodiment, if the input biometric information and the owner biometric information are sufficiently similar to determine that they belong to the same individual, and the input biometric information and the auxiliary biometric information (e.g., biometric information similar to the owner biometric information) are not sufficiently similar to determine that they belong to the same individual, the matching module 420 can recognize that the input biometric information belongs to the owner.

[0112] For example, if the owner's input biometric information is recognized in the primary biometric recognition, the matching module 420 can match some or all of the input feature values with the auxiliary feature values through the second matcher 423. If a matching result of the second matcher 423 satisfies a preset threshold condition, the matching module 420 can compare a first similarity (e.g., a main similarity) with a second similarity (e.g., an auxiliary similarity), and finally determine whether the input biometric information belongs to the owner based on a comparison result. The first similarity can indicate a similarity between the input feature values and the owner feature values, and the second similarity can indicate a similarity between the input feature values and the auxiliary feature values. If the first similarity is higher than the second similarity, the matching module 420 can finally determine the owner's input biometric information.

[0113] According to an embodiment, if the input biometric information and the owner biometric information are sufficiently similar to determine that they belong to the same individual, the input biometric information and the auxiliary biometric information are also sufficiently similar to determine that they belong to the same individual, and the input biometric information is more similar to the owner biometric information than to the auxiliary biometric information, the matching module 420 can determine the owner's input biometric information.

[0114] For ease of understanding, it has been described in Figure 4 that the matching module 420 or the first matcher 421 or the second matcher 423 of the matching module 420 autonomously performs a matching operation. However, the corresponding operation can be substantially performed by the processor, the matching module 421, the first matcher 421, or the second matcher 423.

[0115] Figure 5 is a flowchart of operations for performing a biometric authentication in an electronic device (e.g., the electronic device 101) according to an embodiment of the disclosure. Figure 1 According to an embodiment, the processor (e.g., the processor 310) of the electronic device 101 can operate as shown in Figure 3 Figure 5

[0116] Referring to Figure 5 , in operation 510 according to an embodiment, the electronic device 101 can generate a main matcher to which a main feature value for biometric authentication is applied. For example, a main feature value extracted from owner biometric information in an initial setting for biometric authentication can be applied to the main matcher. The main feature value can be obtained by extracting a feature from a plurality of main feature points from the owner biometric information. The main feature points can be determined as feature points that can be used to easily distinguish a person from the owner in the owner biometric information.

[0117] In operation 520 according to an embodiment, the electronic device 101 can collect auxiliary biometric information. The auxiliary biometric information can be biometric information that is highly likely to be misidentified as the owner biometric information. The auxiliary biometric information can be biometric information extracted from, for example, a face image of a family member or a relative. The main matcher generated in operation 510 can be used to collect the auxiliary biometric information. For example, the main matcher can be used to confirm a similarity between target biometric information and the owner biometric information, and designate and / or store target biometric information selected based on the confirmed similarity as the auxiliary biometric information.

[0118] ​​According to an embodiment, after the owner biometric information is stored, the electronic device 101 can collect the auxiliary biometric information from the pre-stored data according to data selected by the user (the owner). The pre-stored data may, for example, be a file stored in the electronic device 101, a file stored in a server connected through a communication network such as the Internet, or an image such as a photo selected from a plurality of photos. If it is difficult to automatically sort other biometric information such as a fingerprint, an iris, or a voice other than a face, the electronic device 101 can request the user to input the auxiliary biometric information and collect the biometric information input in response to the request as the auxiliary biometric information.

[0119] For example, the electronic device 101 can collect biometric information selected by the user from the stored biometric information or newly input as the auxiliary biometric information. The stored biometric information may, for example, be an image stored in the electronic device 101 or an image stored in a server accessed via a communication network such as the Internet. The newly input biometric information can be obtained by requesting the user to additionally store biometric information such as a fingerprint, an iris, or a voice for which automatic similar data sorting is difficult.

[0120] For example, the electronic device 101 can compare pre-stored images with an image of the owner stored as the owner biometric information and collect auxiliary biometric information from the pre-stored images that can contain two or more faces having a high degree of similarity. Herein, the auxiliary biometric information can be obtained from at least one similar face among two or more similar faces in the pre-stored images, except for the face most similar to the owner biometric information. The auxiliary biometric information can be obtained by, for example, converting an image of a subject corresponding to the similar face into a digital signal.

[0121] In another example, if biometric recognition has continuously failed a preset number of times (e.g., 5 times) and the device is locked, the electronic device 101 can store at least one biometric information of the failed biometric recognition as the auxiliary biometric information after the lock of the device is released. To store at least one biometric information of the failed biometric recognition as the auxiliary biometric information, the electronic device 101 can recommend the user to store the corresponding biometric information as the auxiliary biometric information. If the user accepts the storage of the biometric information in response to the recommendation, the electronic device 101 can store the corresponding biometric information as the auxiliary biometric information.

[0122] In yet another example, if biometric recognition with multi-modal biometric information (e.g., iris + face) is supported and biometric recognition using biometric information of a first type (e.g., iris) fails, the electronic device 101 can store biometric information of a second type (e.g., face) as auxiliary biometric information. The types of biometric information of the multi-modal biometric information are not limited to iris and face, but can be fingerprint, iris, retina, palm shape, face, voice, and / or signature.

[0123] According to an embodiment, in operation 530, the electronic device 101 can generate an auxiliary matcher to which an auxiliary feature value is applied. The auxiliary feature value to be applied to the auxiliary matcher can be generated through the collected auxiliary biometric information. To generate the auxiliary matcher, the electronic device 101 can extract the auxiliary feature value from the collected auxiliary biometric information. The electronic device 101 can extract the auxiliary feature value by using a feature value extraction requirement (e.g., the same feature extractor) used to extract the primary feature value. The electronic device 101 can compare the extracted auxiliary feature value with the primary feature value, and sort the extracted auxiliary feature value in descending order of feature value difference based on the comparison. The electronic device 101 can select one or more auxiliary feature values (e.g., N-ary feature values) that are distinguished from the primary feature value among the sorted auxiliary feature values. For example, the electronic device 101 can determine a threshold value for distinguishing from the primary feature value, and select N-ary (less than M) auxiliary feature values that satisfy the determined threshold value among the sorted M-ary auxiliary feature values. The electronic device 101 can generate the auxiliary matcher by storing and / or applying the selected N-ary auxiliary feature values to the auxiliary matcher. In a secondary match for confirming whether the input biometric information is the owner biometric information, the auxiliary matcher can be used to further determine similarity with the input feature value by using only the N-ary feature values.

[0124] According to an embodiment, in operation 540, the electronic device 101 can perform biometric authentication for confirming whether the input biometric information is the owner biometric information using the primary matcher and / or the auxiliary matcher. For example, if input biometric information for requesting biometric recognition is received, the electronic device 101 can extract an input feature value from the input biometric information. The input feature value can be extracted by applying the same or similar conditions (e.g., using one feature extractor) of the primary feature value or the auxiliary feature value.

[0125] According to an embodiment, the electronic device 101 can acquire a first similarity by matching the input feature value and the primary feature value through the primary matcher. The electronic device 101 can determine whether the first similarity satisfies a first critical condition. The first critical condition can be set, stored, or designated to determine whether the input biometric information is the owner biometric information. The first critical condition can be a key factor for determining a biometric false recognition rate. For example, if the first critical condition is set higher, the biometric false recognition rate (e.g., a rate at which input biometric information of another person is erroneously recognized as the owner biometric information) can decrease, but the processing time for biometric recognition can be delayed. In contrast, if the first critical condition is set lower, the processing time for biometric recognition can be reduced, but the biometric false recognition rate can increase. Accordingly, it is necessary to optimize the first critical condition by considering both the false recognition rate and the delay time. Certain embodiments of the present disclosure can reduce the false recognition rate and reduce the processing time for biometric recognition.

[0126] According to an embodiment, if the first similarity satisfies the first critical condition, the electronic device 101 can acquire a second similarity by matching some or all of the input feature value and the secondary feature value through the secondary matcher. The electronic device 101 can determine whether the second similarity satisfies a second critical condition. The second critical condition can be set, stored, or designated to determine whether the input biometric information and the secondary biometric information (biometric information similar to the owner) belong to the same person. The second critical condition can be a key factor for determining a biometric false recognition rate. For example, if the second critical condition is set higher, the processing time for biometric recognition can be reduced, but the biometric false recognition rate can increase. In contrast, if the second critical condition is set lower, the biometric false recognition rate can decrease, but the processing time for biometric recognition can be delayed. Accordingly, it is necessary to optimize the second critical condition by considering both the false recognition rate and the delay time. Certain embodiments of the present disclosure can reduce the false recognition rate and reduce the processing time for biometric recognition.

[0127] According to an embodiment, if it is determined through the primary matcher that the input biometric information is the owner biometric information (or that the target of the input biometric information is the same person as the owner) and it is determined through the secondary matcher that the input biometric information is not the secondary biometric information (or that the target of the input biometric information is different from the target of the biometric information similar to the owner), the electronic device 101 can finally determine that the input biometric information is the owner biometric information. As mentioned above, by verifying whether the target of the input biometric information is different from another person who has biometric information similar to the owner through the secondary matcher, biometric false recognition can be reduced.

[0128] According to an embodiment, to further reduce the biological misrecognition rate, the electronic device 101 can perform an additional determination regarding the first similarity and the second similarity. For example, if the auxiliary matcher determines that the target of the input biological feature information is a person similar to the owner (e.g., a family member) and determines that the first similarity is higher than the second similarity, the electronic device 101 can determine that the target of the input biological feature information is the owner, not the similar person.

[0129] Figure 6 is a diagram 600 illustrating a biological feature recognition in an electronic device (e.g., Figure 1 101) according to an embodiment of the disclosure. The biological feature recognition can be completed in the order of "acquisition → extraction → comparison → similarity determination." According to an embodiment, facial recognition is assumed in Figure 6 . However, it is noted that other types of biological feature information (e.g., fingerprint, iris, signature, palm shape) can be employed.

[0130] Referring to Figure 6 , the electronic device 101 according to an embodiment can load an owner image 611 and an input image 621. The input image 621 can be an image input to attempt biological feature authentication or an auxiliary image input to further verify the biological feature recognition. The auxiliary image, for example, can be a photo including a face of a person (e.g., a hacker) having similar biological feature information to the owner.

[0131] According to an embodiment, the electronic device 101 can acquire a first face image from the owner image 611 (operation 613) and acquire a second face image from the input image 621 (operation 623). The electronic device 101 can pre-define, store, or designate a region in the owner image and a region in the input image to acquire the first face image and the second face image.

[0132] According to an embodiment, the electronic device 101 can determine first feature points of the first face image acquired from the owner image 611 (operation 615) and determine second feature points of the second face image acquired from the input image 621 (operation 625). The first feature points and the second feature points can or can not be located at the same points in the corresponding face images. The first feature points and the second feature points can be determined as positions having distinguishing features to distinguish two faces in the corresponding face images (e.g., left and right corners of eyes, left and right corners of a nose). The first feature points and the second feature points can be determined by recognizing pre-designated points and extracting all or some of the recognized points. Each feature point can include data (e.g., a feature value) for distinguishing a shape, a size, or some other element in the image.

[0133] According to an embodiment, the electronic device 101 can align the first face image and the second face image by considering the first feature point and the second feature point (operation 617 and operation 627). The alignment may, for example, align the first face image and the second face image, which are graphic elements, with a virtual reference line. The alignment can make a feature comparison of the first face image and the second face image easy.

[0134] According to an embodiment, the electronic device 101 can extract a first feature value (e.g., a primary feature value or a secondary feature value) from the aligned first face image feature point, extract a feature value from the second feature point (e.g., an input feature value or a secondary feature value) of the aligned second face image, and match the first feature value with the second feature value (operation 630).

[0135] According to an embodiment, based on a matching result of the first feature value and the second feature value, the electronic device 101 can process (e.g., pre-process) the first face image and the second face image (operation 640 and operation 650). The electronic device 101 can acquire a similarity by comparing the first feature value and the second feature value of the processed first face image and second face image (operation 660). Based on the acquired similarity, the electronic device 101 can determine success (operation 670) or failure (operation 680) of the biometric authentication of the input image 621.

[0136] Figure 7 is a diagram 700 illustrating an operation of applying a secondary feature value to a secondary matcher in an electronic device (e.g., the electronic device 101) according to an embodiment of the disclosure. Figure 1 According to an embodiment, the operation (e.g., operation 530 of FIG. 5) of applying a secondary feature value to a secondary matcher in an electronic device 101 can be substantially performed by a processor (e.g., the processor 120 of FIG. 1, Figure 5 According to an embodiment, the operation (e.g., operation 530 of FIG. 5) of applying a secondary feature value to a secondary matcher in an electronic device 101 can be substantially performed by a processor (e.g., the processor 120 of FIG. 1, Figure 1 According to an embodiment, the operation (e.g., operation 530 of FIG. 5) of applying a secondary feature value to a secondary matcher in an electronic device 101 can be substantially performed by a processor (e.g., the processor 120 of FIG. 1, Figure 3 According to an embodiment, the operation (e.g., operation 530 of FIG. 5) of applying a secondary feature value to a secondary matcher in an electronic device 101 can be substantially performed by a processor (e.g., the processor 120 of FIG. 1, Figure 4 According to an embodiment, the operation (e.g., operation 530 of FIG. 5) of applying a secondary feature value to a secondary matcher in an electronic device 101 can be substantially performed by a processor (e.g., the processor 120 of FIG. 1, Figure 7 According to an embodiment, the operation (e.g., operation 530 of FIG. 5) of applying a secondary feature value to a secondary matcher in an electronic device 101 can be substantially performed by a processor (e.g., the processor 120 of FIG. 1,

[0137] Referring to FIG. 7, Figure 7 In operation 710 according to an embodiment, the electronic device 101 can extract a feature value (e.g., a primary feature value, an input feature value, or a secondary feature value) from biometric information (e.g., owner biometric information, input biometric information, or secondary biometric information).

[0138] In operation 720a according to an embodiment, the electronic device 101 can generate a primary feature vector using a primary feature value extracted from the owner biometric information. In operation 720b according to an embodiment, the electronic device 101 can generate a secondary feature vector using a secondary feature value extracted from the secondary biometric information.

[0139] In operation 730 according to an embodiment, the electronic device 101 can compare the primary feature vector with the auxiliary feature vector, and determine a feature value to be applied to the auxiliary matcher based on a comparison result. In operation 731, the electronic device 101 can compare the primary feature vector and the auxiliary feature vector, for example, by feature point. In operation 733, the electronic device 101 can sort the auxiliary feature values of the auxiliary feature vector in descending order of, for example, feature value difference, based on the comparison.

[0140] Table 2 shows an example of the sorted auxiliary feature values.

[0141] [Table 2]

[0142] Maximum Feature 10 ..... ..... Feature x ..... ..... Minimum Feature 7

[0143] Referring to Table 2, the tenth auxiliary feature value has the greatest difference from the primary feature value, and the seventh auxiliary feature value has the most similar difference from the primary feature value.

[0144] In operation 735 according to an embodiment, the electronic device 101 can select an N-element auxiliary feature value that distinguishes from the primary feature value among the sorted auxiliary feature values. For example, the electronic device 101 can determine a threshold value for distinguishing from the primary feature value, and select an N-element (less than M) auxiliary feature value that satisfies the determined threshold value among the sorted M-element auxiliary feature values.

[0145] In operation 737 according to an embodiment, the electronic device 101 can determine a similarity between the owner biometric information and the auxiliary biometric information based on the selected N-element auxiliary feature value, and thus determine an auxiliary feature value to be applied to the auxiliary matcher. In the secondary matching for confirming whether the input biometric information is the owner biometric information, the auxiliary matcher can be used to further determine a similarity with the input feature value by using only the N-element feature value.

[0146] Figure 8 is a flowchart illustrating an example of operations (e.g., operations 540 of FIG. 5) for performing biometric recognition in an electronic device (e.g., the electronic device 101 of FIG. 1) according to an embodiment of the disclosure. Figure 1 is a flowchart illustrating an example of operations (e.g., operations 540 of FIG. 5) for performing biometric recognition in an electronic device (e.g., the electronic device 101 of FIG. 1) according to an embodiment of the disclosure. Figure 5 is a flowchart illustrating an example of operations (e.g., operations 540 of FIG. 5) for performing biometric recognition in an electronic device (e.g., the electronic device 101 of FIG. 1) according to an embodiment of the disclosure. Figure 1 is a flowchart illustrating an example of operations (e.g., operations 540 of FIG. 5) for performing biometric recognition in an electronic device (e.g., the electronic device 101 of FIG. 1) according to an embodiment of the disclosure. Figure 3 is a flowchart illustrating an example of operations (e.g., operations 540 of FIG. 5) for performing biometric recognition in an electronic device (e.g., the electronic device 101 of FIG. 1) according to an embodiment of the disclosure. Figure 4 is a flowchart illustrating an example of operations (e.g., operations 540 of FIG. 5) for performing biometric recognition in an electronic device (e.g., the electronic device 101 of FIG. 1) according to an embodiment of the disclosure. Figure 8 is a flowchart illustrating an example of operations (e.g., operations 540 of FIG. 5) for performing biometric recognition in an electronic device (e.g., the electronic device 101 of FIG. 1) according to an embodiment of the disclosure.

[0147] Referring to Figure 8According to an embodiment, the electronic device 101 can receive an owner image corresponding to the owner biometric information in operation 811, receive an input image corresponding to the authentication target biometric information in operation 813, and receive an auxiliary image corresponding to a hacker biometric information having similar biometric information as the owner in operations 815a to 815c.

[0148] In operation 820 according to an embodiment, the electronic device 101 can extract a main feature value from the owner image, extract an input feature value from the input image, and extract an auxiliary feature value from the auxiliary image.

[0149] According to an embodiment, the electronic device 101 can generate a main feature vector having the main feature value in operation 831, generate an input feature vector having the input feature value in operation 833, and generate an auxiliary feature vector having the auxiliary feature value in operation 835.

[0150] In operation 841 according to an embodiment, the electronic device 101 can compare the main feature value with the input feature vector, and determine a first similarity between the owner biometric information and the authentication target biometric information based on a comparison result. In operation 843 according to an embodiment, the electronic device 101 can compare the input feature value with the auxiliary feature vector, and determine a second similarity between the authentication target biometric information and the hacker biometric information based on a comparison result.

[0151] According to an embodiment, the electronic device 101 can determine success (operation 851) or failure (operation 853) of biometric recognition of the authentication target based on the determined first similarity. According to an embodiment, the electronic device 101 can determine success (operation 855) or failure (operation 857) of biometric recognition of the authentication target based on the determined second similarity. That is, the electronic device 101 can complete biometric recognition of the authentication target by primary determination using the owner biometric information and secondary determination using the auxiliary biometric information.

[0152] In operation 860 according to an embodiment, only when biometric recognition using the first similarity is successful and biometric recognition using the second similarity is failed, the electronic device 101 can finally recognize that the input biometric information matches the owner biometric information (biometric recognition is successful).

[0153] Figure 9 is a flowchart illustrating operations (e.g., operations 811 to 815) for performing biometric recognition in an electronic device (e.g., the electronic device 101) according to an embodiment of the disclosure. Figure 1 of the electronic device 101) according to an embodiment of the disclosure. Figure 5FIG. 900 is a diagram illustrating another example of the operation 540 of FIG. 8 according to an embodiment of the disclosure. According to an embodiment, the operation of FIG. 900 can be substantially the same as the operation 540 of FIG. 8. According to an embodiment, the operation of FIG. 900 can be substantially performed by the processor 120 of the electronic device 101 (e.g., the processor 120 of FIG. 2, Figure 1 the processor 310 of the electronic device 101, Figure 3 the processor 310 of the electronic device 101, Figure 4 the matching module 420 of the electronic device 101. Figure 9 the operation of FIG. 8.

[0154] Referring to FIG. 9, Figure 9 In operation 910 according to an embodiment, the electronic device 101 can extract a main feature value from an owner image corresponding to owner biometric information, extract an input feature value from an input image corresponding to authentication target biometric information, and extract an auxiliary feature value from an auxiliary image corresponding to hacker biometric information having a similar biometric information to the owner.

[0155] According to an embodiment, the electronic device 101 can generate a main feature vector having the main feature value in operation 921, generate an input feature vector having the input feature value in operation 923, and generate an auxiliary feature vector having the auxiliary feature value in operation 925.

[0156] In operation 931 according to an embodiment, the electronic device 101 can compare the main feature vector with the input feature vector and determine a first similarity based on a comparison result. In operation 933 according to an embodiment, the electronic device 101 can compare the input feature vector with the auxiliary feature vector and determine a second similarity based on a comparison result.

[0157] According to an embodiment, the electronic device 101 can determine success (operation 941) or failure (operation 943) of biometric recognition of the input image based on the determined first similarity. According to an embodiment, the electronic device 101 can determine success (operation 945) or failure (operation 947) of biometric recognition of the input image based on the determined second similarity. For example, if biometric authentication based on the first similarity is successful and biometric authentication based on the second similarity is failed, the electronic device 101 can determine that the input biometric information matches the owner biometric information (biometric recognition of the input image is successful). Alternatively, if biometric authentication based on the first similarity is failed and biometric authentication based on the second similarity is successful, the electronic device 101 can determine that the input biometric information does not match the owner biometric information (biometric recognition of the input image is failed).

[0158] In operation 950 according to the embodiment, if both biometric identification using the first similarity and the second similarity are successful, the electronic device 101 can ultimately determine whether the biometric identification was successful (whether the biometric identification of the input image was successful) by comparing the first similarity and the second similarity.

[0159] According to an embodiment, if biometric identification using the second similarity is successful but the first similarity is relatively higher than the second similarity, the electronic device 101 can determine that the input biometric information matches the owner's biometric information (operation 961). Otherwise, if the first similarity is not relatively higher than the second similarity, the electronic device 101 can determine that the input biometric information does not match the owner's biometric information (operation 963).

[0160] In operation 790 according to an embodiment, electronic device 101 may determine that the first similarity is relatively higher than the second similarity, and thus ultimately identify that the input biometric information matches the owner's biometric information (the input image is the owner's image).

[0161] According to various embodiments of this disclosure, electronic devices using biometric identification can improve security and increase user confidence in biometric authentication by achieving a false acceptance rate (FAR) exceeding a certain level.

[0162] Electronic devices according to embodiments (e.g., Figure 1 The electronic device 101 shown may include: one or more memories; and at least one processor for accessing one or more memories, wherein at least one of the memories stores instructions that, when executed, cause the at least one processor to: determine an M-ary feature point to obtain a first feature value from the owner's biometric information, and confirm auxiliary biometric information from biometric information stored in at least one of one or more memories or an external server, wherein the auxiliary biometric information has a feature value at the M-ary feature point that is similar to that of the owner's biometric information.

[0163] According to an embodiment, auxiliary biometric information is selected by a user or at least one of a first matcher that has applied a first feature value.

[0164] According to an embodiment, the instructions cause at least one processor to recognize the input biometric information of the failed biometric authentication as additional auxiliary biometric information when biometric authentication using the input biometric information fails.

[0165] According to an embodiment, the instructions cause the at least one processor to, in biometric authentication using the first biometric information of the first type and the second biometric information of the second type together, confirm the second biometric information as additional auxiliary biometric information when the biometric authentication using the first biometric information fails.

[0166] According to an embodiment, the instructions cause the at least one processor to: obtain an M-ary second feature value from the auxiliary biometric information by applying an M-ary feature point for obtaining the first feature value from the owner biometric information to the auxiliary biometric information, select an N-ary third feature value corresponding to the N-ary feature point among the M-ary second feature values by considering similarity between the M-ary second feature values and the first feature value, and apply the N-ary third feature value to the second matcher.

[0167] According to an embodiment, the instructions cause the at least one processor to select, as the N-ary third feature value, a feature value having a similarity lower than a first threshold value with the first feature value among the M-ary second feature values.

[0168] According to an embodiment, the instructions cause the at least one processor to: sort the M-ary second feature values based on similarity between the M-ary second feature values and the first feature value, and select the N-ary third feature value from the M-ary second feature values based on the sorting.

[0169] According to an embodiment, the instructions cause the at least one processor to: obtain a fourth feature value corresponding to the M-ary feature point from input biometric information; perform first biometric authentication on the input biometric information by matching the first feature value and the fourth feature value using the first matcher; when the first biometric authentication using the first matcher is successful, perform second biometric authentication by matching an N-ary fifth feature value corresponding to the N-ary feature point among the fourth feature values with the N-ary third feature value using the second matcher, and when the second biometric authentication fails, approve biometric authentication on the input biometric information.

[0170] According to an embodiment, the instructions cause the at least one processor to: when the second biometric authentication is successful, compare a first similarity between the first feature value and the fourth feature value with a second similarity between the N-ary third feature value and the N-ary fifth feature value, and when the first similarity is higher than the second similarity, approve biometric authentication on the input biometric information.

[0171] According to an embodiment, the instructions cause the at least one processor to: confirm the owner biometric information by converting an owner's face image into a digital form, wherein the auxiliary biometric information includes at least one face having a feature value having a similarity higher than a threshold value with the first feature value.

[0172] An electronic device (e.g., the electronic device 101) according to an embodiment can include a communication interface configured to receive owner biometric information from an external server, a biometric information storage configured to store the owner biometric information, a biometric information processor configured to determine an M-nomial feature point to obtain a first feature value from the owner biometric information, to confirm auxiliary biometric information from biometric information stored in at least one of one or more memories or the external server, and to perform biometric authentication on input biometric information based on the owner biometric information and the auxiliary biometric information, and a biometric information matching unit configured to apply the first feature value to a first matcher. Figure 1 The biometric authentication method in the electronic device 101 shown can include determining an M-nomial feature point to obtain a first feature value from owner biometric information, confirming auxiliary biometric information from biometric information stored in at least one of one or more memories or an external server, and performing biometric authentication on input biometric information based on the owner biometric information and the auxiliary biometric information, wherein the auxiliary biometric information has similar feature values to the owner biometric information at the M-nomial feature point.

[0173] According to an embodiment, the auxiliary biometric information is selected by at least one of a user or a first matcher to which the first feature value is applied.

[0174] According to an embodiment, the method can further include, when the biometric authentication with the input biometric information fails, confirming the input biometric information of the failed biometric authentication as additional auxiliary biometric information.

[0175] According to an embodiment, the method can further include, in biometric authentication using first biometric information of a first type and second biometric information of a second type together, when the biometric authentication through the first biometric information fails, identifying the second biometric information as additional auxiliary biometric information.

[0176] According to an embodiment, performing the biometric authentication further includes obtaining an M-nomial second feature value from the auxiliary biometric information by applying the M-nomial feature point for obtaining the first feature value from the owner biometric information to the auxiliary biometric information, selecting an N-nomial third feature value corresponding to the N-nomial feature point among the M-nomial second feature values by considering similarity between the second feature value and the first feature value, and applying the N-nomial third feature value to the second matcher.

[0177] According to an embodiment, the method can further include selecting, as an N-nomial fourth feature value, a feature value having a similarity lower than a first threshold value among the M-nomial second feature values.

[0178] According to an embodiment, selecting the fourth feature value includes sorting the M-nomial second feature values based on similarity between the M-nomial second feature values and the first feature value, and selecting the N-nomial fourth feature value from the M-nomial second feature values based on the sorting.

[0179] According to an embodiment, the performing the biometric authentication further includes: obtaining fourth feature values corresponding to the M-ary feature points from the input biometric information; performing first biometric authentication on the input biometric information by matching the first feature values and the fourth feature values using the first matcher; when the first biometric authentication using the first matcher is successful, performing second biometric authentication on the input biometric information by matching fifth feature values corresponding to the N-ary feature points among the fourth feature values and the N-ary third feature values using the second matcher; and when the second biometric authentication fails, approving the biometric authentication on the input biometric information.

[0180] According to an embodiment, the method can further include, when the second biometric authentication is successful, comparing a first similarity between the first feature values and the fourth feature values with a second similarity between the N-ary third feature values and the fifth feature values; and when the first similarity is higher than the second similarity, approving the biometric authentication on the input biometric information.

[0181] According to an embodiment, the extracting the second feature values further includes confirming the owner biometric information by converting the owner's face image into a digital form; wherein the auxiliary biometric information includes at least one face having a feature value having a similarity higher than a threshold value with the first feature values.

[0182] Certain aspects of the above-described embodiments of this disclosure can be implemented in hardware, firmware, or via the execution of computer code that can be stored in a recording medium such as a CD-ROM, a digital versatile disk (DVD), a magnetic tape, a RAM, a floppy disk, a hard disk, or a magneto-optical disk, or initially stored on a remote recording medium or a non-transitory machine-readable medium and downloaded and stored locally over a network, so that the methods described herein can be presented using a general purpose computer, or a special purpose processor, via such software or in programmable or dedicated hardware such as an ASIC or an FPGA. As will be understood in the art, the computer, processor, microprocessor controller, or programmable hardware includes memory components, e.g., RAM, ROM, flash, etc. that can store or receive software or computer code that when accessed and executed by the computer, processor, or hardware implement the processing methods described herein.

[0183] While the disclosure has been illustrated and described with reference to various embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents.

Claims

1. An electronic device, the electronic device comprising: Memory; as well as At least one processor, The memory stores instructions that, when executed by the at least one processor, cause the electronic device to: Receives biometric information input from the user of the electronic device. Determine M-ary feature points for obtaining a first feature value from the owner's biometric information of the owner of the electronic device. The auxiliary biometric information is confirmed from the biometric information of a third party, and the auxiliary biometric information is stored in the memory or an external server. The user is authenticated at least in part based on a comparison of the input biometric information with the owner's biometric information and the auxiliary biometric information. The auxiliary biometric information has a feature value at the M-ary feature point that is similar to that of the owner's biometric information.

2. The electronic device according to claim 1, wherein, The auxiliary biometric information is selected by either the user or at least one of the first matchers that has applied the first feature value.

3. The electronic device according to claim 1, wherein, The instruction further enables the electronic device to: When biometric authentication using the input biometric information fails, the input biometric information is recognized as additional auxiliary biometric information. In biometric authentication performed using first biometric information of the first type and second biometric information of the second type, when biometric authentication using the first biometric information fails, the second biometric information is recognized as additional auxiliary biometric information.

4. The electronic device according to claim 2, wherein, The instruction further enables the electronic device to: By applying the M-ary feature points used to obtain the first feature value from the owner's biometric information to the auxiliary biometric information, an M-ary second feature value is obtained from the auxiliary biometric information. Among the M-ary second feature values, feature values ​​whose similarity to the first feature value is lower than a first threshold are selected as N-ary third feature values ​​corresponding to the N-ary feature points. The N-ary third feature value is applied to the second matcher.

5. The electronic device according to claim 4, wherein, The instruction further enables the electronic device to: The M-ary second feature values ​​are sorted based on the similarity between the M-ary second feature values ​​and the first feature values, and The N-ary third feature value is selected from the M-ary second feature values ​​based on the sorting.

6. The electronic device according to claim 5, wherein, The instruction further enables the electronic device to: The fourth feature value corresponding to the M-ary feature point is obtained from the input biometric information. First biometric authentication is performed on the input biometric information by matching the first feature value and the fourth feature value using the first matcher. When the first biometric authentication using the first matcher is successful, the second matcher is used to match the fifth feature value corresponding to the N-ary feature point in the fourth feature value with the N-ary third feature value, thereby performing a second biometric authentication on the input biometric information. When the second biometric authentication fails, biometric authentication of the input biometric information is approved. When the second biometric authentication is successful, the first similarity between the first feature value and the fourth feature value is compared with the second similarity between the N-ary third feature value and the fifth feature value. When the first similarity is higher than the second similarity, biometric authentication of the input biometric information is approved.

7. The electronic device according to claim 1, wherein, The instruction further enables the electronic device to: By converting the owner's facial image into digital form, the owner's biometric information can be verified. The auxiliary biometric information includes at least one face, and the at least one face has a feature value that has a similarity to the first feature value that is higher than a threshold.

8. A method for biometric authentication in an electronic device, the method comprising: Receives biometric information input from the user of the electronic device. Determine M-ary feature points for obtaining a first feature value from the owner's biometric information of the owner of the electronic device; The auxiliary biometric information is confirmed from the biometric information of a third party, and the auxiliary biometric information is stored in a memory or an external server; as well as The user is authenticated at least in part based on a comparison of the input biometric information with the owner's biometric information and the auxiliary biometric information. The auxiliary biometric information has a feature value at the M-ary feature point that is similar to that of the owner's biometric information.

9. The method according to claim 8, wherein, The auxiliary biometric information is selected by either the user or at least one of the first matchers that has applied the first feature value.

10. The method according to claim 8, further comprising: When biometric authentication using the input biometric information fails, the input biometric information is recognized as additional auxiliary biometric information. as well as In biometric authentication performed using first biometric information of the first type and second biometric information of the second type, when biometric authentication performed using the first biometric information fails, the second biometric information is recognized as additional auxiliary biometric information.

11. The method according to claim 9, wherein, Authenticating the user further includes: By applying the M-ary feature points used to obtain the first feature value from the owner's biometric information to the auxiliary biometric information, an M-ary second feature value is obtained from the auxiliary biometric information; By considering the similarity between the M-ary second feature value and the first feature value, an N-ary third feature value corresponding to the N-ary feature point is selected from the M-ary second feature value; and The N-ary third feature value is applied to the second matcher.

12. The method according to claim 11, wherein, Selecting the N-ary third eigenvalue also includes: The M-ary second feature value is sorted based on the similarity between the M-ary second feature value and the first feature value; The N-ary third feature value is selected from the M-ary second feature values ​​based on the sorting.

13. The method according to claim 12, wherein, Authentication of the user also includes: Obtain the fourth feature value corresponding to the M-ary feature point from the input biometric information; First biometric authentication is performed on the input biometric information by matching the first feature value and the fourth feature value using the first matcher; When the first biometric authentication using the first matcher is successful, the second biometric authentication is performed on the input biometric information by matching the fifth feature value corresponding to the N-ary feature point in the fourth feature value with the N-ary third feature value using the second matcher; and When the second biometric authentication fails, biometric authentication of the input biometric information is approved.

14. The method of claim 13, further comprising: When the second biometric authentication is successful, the first similarity between the first feature value and the fourth feature value is compared with the second similarity between the N-ary third feature value and the fifth feature value; as well as When the first similarity is higher than the second similarity, biometric authentication of the input biometric information is approved.

15. The method according to claim 8, wherein, The method further includes: By converting the owner's facial image into digital form, the owner's biometric information can be verified. The auxiliary biometric information includes at least one face, and the at least one face has a feature value that has a similarity to the first feature value that is higher than a threshold.

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