Imaging target identification method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an imaging target recognition method and device, electronic equipment and a storage medium. The method comprises the steps that multiple echo data corresponding to an imaging target and generated by a fingerprint sensor according to multiple preset configurations are acquired; and identifying the material of the imaging target according to the plurality of echo data. According to the embodiment of the invention, based on the fact that the same material has different echo signals in different configurations, multiple pieces of echo data of the imaging target are obtained through multiple preset configurations, the multiple pieces of echo data are combined to better distinguish the material of the imaging target, and a better recognition effect is achieved. In the fingerprint anti-counterfeiting application, a real finger and a fake finger can be better identified. In the display screen protective film identification application, the material of the protective film can be identified.

Description

Technical Field

[0001] This application relates to the technical field of electronic devices, and in particular, to an imaging target recognition method, apparatus, electronic device, and storage medium. Background Art

[0002] Biometric systems are widely used to improve the security of electronic devices. In particular, fingerprint recognition systems are widely used in personal electronic devices such as mobile phones. The fingerprint recognition system can be used for unlocking electronic devices, mobile payment, account login, real-name authentication, etc.

[0003] Fingerprint anti-counterfeiting is an important issue for fingerprint recognition systems. Fingerprint anti-counterfeiting refers to preventing the imitation of live fingerprints. If a live fingerprint is successfully imitated, unauthorized unlocking, payment, and account login of the system may be wrongly approved, which may lead to serious consequences.

[0004] In an ultrasonic fingerprint system, due to the different acoustic impedances of different materials, different materials have different responses under the same configuration. In related technologies, images of a single configuration are collected to distinguish imaging targets with different acoustic impedances. Since the acoustic impedances of some materials are close to that of a real finger, the response discrimination of these materials under the same configuration is low, and imaging targets with different acoustic impedances cannot be completely distinguished. Summary of the Invention

[0005] In view of the above problems, embodiments of the present application provide an imaging target recognition method, apparatus, electronic device, and storage medium to solve the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides an imaging target recognition method, which is applied to an electronic device. The method includes: obtaining a plurality of echo data corresponding to an imaging target respectively generated by a fingerprint sensor according to a plurality of preset configurations; and identifying the material of the imaging target according to the plurality of echo data.

[0007] In some embodiments, identifying the material of the imaging target according to the plurality of echo data includes: calculating one or more preset combined index values according to the plurality of echo data, where at least one combined index value is calculated by combining the indexes of at least two echo data; and identifying the material of the imaging target according to the one or more combined index values and the index value range corresponding to the material.

[0008] In some embodiments, identifying the material of the imaging target according to one or more combined index values includes: determining whether one or more combined index values meet the index value range of a target material, and determining whether the imaging target is the target material according to the determination result.

[0009] In some embodiments, the echo data is an image, and the metrics of the echo data include at least one of the image signal amount, the image signal-to-noise ratio, the signal sensitivity, the imaging sensitivity, the magnitude of the alternating current (AC) signal in the image, the magnitude of the AC signal in the image after removing the background pattern, the image fluctuation information calculated in the spatial domain of the image, and the data magnitude of the image. The signal sensitivity is the image signal amount obtained by a first integration, and the imaging sensitivity is the amount of the AC signal increased by a first integration.

[0010] In some embodiments, identifying the material of the imaging target based on the plurality of echo data includes: inputting the plurality of echo data into an imaging target recognition model, so that the imaging target recognition model outputs a model output indicating the material of the imaging target.

[0011] In some embodiments, the model output is a score indicating that the imaging target is the target material; or the model output is the material information of the imaging target.

[0012] In some embodiments, the imaging target recognition model is a neural network model, and the neural network model is trained to extract the joint features of the plurality of echo data respectively generated based on a plurality of preset configurations to obtain the model output.

[0013] In some embodiments, the acoustic wave frequencies and / or the flight times of any two of the plurality of preset configurations are different.

[0014] In some embodiments, before obtaining the plurality of echo signals corresponding to the imaging target respectively generated by the fingerprint sensor according to the plurality of preset configurations, the method further includes: for each of the plurality of preset configurations: using the fingerprint sensor to emit an acoustic wave signal with the acoustic wave frequency of the preset configuration; using the fingerprint sensor to receive the echo signal reflected by the imaging target according to the flight time of the preset configuration and output the echo data corresponding to the preset configuration.

[0015] In some embodiments, the method further includes: traversing the configurations in the configuration space, and for each configuration: generating echo data of a test imaging target according to the configuration, and calculating a plurality of metrics of the echo data to obtain a plurality of evaluation metrics of the configuration; the test imaging target includes the fingerprint of a real finger and the fingerprint of a fake finger; selecting the plurality of preset configurations from the configuration space according to the plurality of evaluation metrics of each configuration in the configuration space.

[0016] In some embodiments, the configuration space is defined by an acoustic wave frequency range and a flight time range, and the configuration includes an acoustic wave frequency and a flight time.

[0017] In some embodiments, generating echo data of a test imaging target according to a configuration includes: transmitting an acoustic wave signal with a configured acoustic wave frequency using a fingerprint sensor; adaptively determining an integration number using the fingerprint sensor, and receiving an echo signal reflected by the test imaging target based on the integration number and the configured flight time, and outputting echo data corresponding to the configuration.

[0018] In some embodiments, selecting the plurality of preset configurations from a configuration space according to multiple evaluation metrics of each configuration in the configuration space includes: for each evaluation metric: obtaining a heat map of the evaluation metric according to the combination of the acoustic wave frequency and the flight time of each configuration, where the heat map characterizes the size distribution of the evaluation metric in different configurations; projecting the heat map into a three-dimensional space according to the evaluation metric value corresponding to each configuration to obtain a three-dimensional heat map; determining the average Euclidean distance from the pixel point corresponding to each configuration in the three-dimensional heat map to other pixel points, and determining candidate configurations corresponding to the evaluation metric based on the average Euclidean distance corresponding to each configuration; and selecting the plurality of preset configurations from the plurality of candidate configurations corresponding to the multiple evaluation metrics.

[0019] In some embodiments, before determining the average Euclidean distance from the pixel point corresponding to each configuration in the three-dimensional heat map to other pixel points, it further includes: identifying and removing singular value points of the evaluation metric in the heat map.

[0020] In some embodiments, identifying the material of the imaging target according to multiple echo data includes: determining whether the imaging target is human epidermal tissue according to the multiple echo data.

[0021] In some embodiments, the fingerprint sensor is located inside or under the display screen, and the method further includes: identifying whether the imaging target is a fingerprint according to multiple echo data; if the imaging target is not a fingerprint and is human epidermal tissue, determining that a non-finger touches the display screen by mistake.

[0022] In some embodiments, the above method is applied to fingerprint anti-counterfeiting, and the imaging target is an input fingerprint, and the input fingerprint includes fingerprints of real fingers and fingerprints of fake fingers.

[0023] In some embodiments, the electronic device includes a display screen, and the method is applied to identify the material of a protective film, and the imaging target is the protective film attached to the surface of the display screen.

[0024] In a second aspect, an embodiment of the present application provides a fingerprint processing device, which includes: a fingerprint sensor, configured to generate multiple echo data corresponding to an imaging target according to multiple configurations respectively; and a processing unit, configured to execute the above imaging target recognition method.

[0025] In a third aspect, an embodiment of the present application provides an electronic device, including: the above fingerprint processing device.

[0026] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause a computer to execute the above-mentioned imaging target recognition method.

[0027] The imaging target recognition method, apparatus, electronic device, and storage medium provided by the embodiments of the present application are based on the fact that the same material has different echo signals in different configurations. By obtaining a plurality of echo data corresponding to the imaging target through a plurality of preset configurations, combining the plurality of echo data can better identify the material of the imaging target and achieve a better recognition effect. In fingerprint anti-counterfeiting applications, real fingers and fake fingers can be better identified. In the application of identifying a display screen protective film, the material of the protective film can be identified.

[0028] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0030] Figure 1A FIG. shows a schematic diagram of an electronic device applicable to an exemplary embodiment of the present application.

[0031] Figure 1B FIG. shows a schematic diagram of another electronic device applicable to an exemplary embodiment of the present application.

[0032] Figure 1C FIG. shows a system block diagram of an electronic device applicable to an exemplary embodiment of the present application.

[0033] Figure 2 FIG. shows a flowchart of the imaging target recognition method according to an exemplary embodiment of the present application.

[0034] Figure 3 FIG. shows a schematic diagram of an acoustic imaging model applicable to an exemplary embodiment of the present application.

[0035] Figure 4 FIG. shows a flowchart of a method for determining a plurality of preset configurations according to an exemplary embodiment of the present application.

[0036] Figure 5 FIG. shows a flowchart of a method for selecting a plurality of preset configurations from a configuration space according to an exemplary embodiment of the present application.

[0037] Figure 6AA heat map of exemplary evaluation metrics of an embodiment of the present application is shown.

[0038] Figure 6B A three-dimensional heat map of exemplary evaluation metrics of an embodiment of the present application is shown.

[0039] Figure 7 A flowchart for identifying an imaging target according to predefined rules is shown.

[0040] Figure 8 A flowchart of a typical fingerprint anti-counterfeiting method of an exemplary embodiment of the present application is shown.

[0041] Figure 9 A flowchart of another typical fingerprint anti-counterfeiting method of an exemplary embodiment of the present application is shown.

[0042] Figure 10 A flowchart of a typical protective film identification method of an exemplary embodiment of the present application is shown.

[0043] Figure 11 A structural block diagram of a fingerprint processing device of an exemplary embodiment of the present application is shown. Detailed implementation manners

[0044] The following details the implementation manners of the present application. The examples of the implementation manners are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The implementation manners described below with reference to the accompanying drawings are exemplary only for explaining the present application and should not be construed as a limitation of the present application.

[0045] In order to enable those skilled in the art of the present technology to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present application.

[0046] In the embodiments of the present application, it should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0047] Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element qualified by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

[0048] In the description of the embodiments of the present application, words such as "example" or "for example" are used to indicate exemplification, illustration or description. Any embodiment or design described as "for example" or "example" in the embodiments of the present application is not construed as being more preferred or having more advantages than another embodiment or design. The use of words such as "example" or "for example" is intended to present relative concepts in a clear manner.

[0049] In addition, "a plurality of" in the embodiments of the present application means two or more. In view of this, "a plurality of" in the embodiments of the present application can also be understood as "at least two". "At least one" can be understood as one or more, for example, understood as one, two or more. For example, including at least one means including one, two or more, and there is no limitation on which ones are included. For example, including at least one of A, B, and C, then what can be included is A, B, C, A and B, A and C, B and C, or A, B, and C.

[0050] It should be noted that in the embodiments of the present application, "and / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / ", unless otherwise specified, generally represents an "or" relationship between the front and rear associated objects.

[0051] It should be pointed out that "connection" in the embodiments of the present application can be understood as electrical connection, and the connection of two electrical components can be a direct or indirect connection between the two electrical components. For example, when A is connected to B, it can be either a direct connection between A and B or an indirect connection between A and B through one or more other electrical components.

[0052] Figure 1A and Figure 1B shows a schematic diagram of an electronic device in which various solutions described herein can be implemented according to an exemplary embodiment of the present application, as Figure 1A and Figure 1BAs shown, the electronic device 100 may include a device body 101 and a fingerprint sensor 102. The fingerprint sensor 102 may collect fingerprint images for fingerprint recognition. The fingerprint sensor 102 may include an ultrasonic fingerprint sensor, which may emit ultrasonic waves to an imaging target and receive echoes, and generate an image of the imaging target based on the echoes. The specific position of the fingerprint sensor 102 in the electronic device 100 may be set on the side, back, front of the device body 101 or under the display screen on the front according to the actual product design requirements.

[0053] In some embodiments, the electronic device 100 may be a portable electronic device, and the portable electronic device may be a smart phone, a tablet computer, a laptop computer, a personal digital assistant, etc. In other embodiments, the electronic device 100 may also be a smart wearable device, such as a smart watch, a virtual reality head-mounted device, an augmented reality head-mounted device, etc. The embodiments of the present application do not limit the type of the electronic device 100.

[0054] In some embodiments, the fingerprint sensor 102 may be specifically set on the side of the device body 101 of the electronic device 100; with the development trend of smart phones or other portable electronic devices towards being thinner and lighter or foldable, the thickness of the electronic device 100 is getting smaller and smaller, resulting in the fingerprint sensor 102 set on the side of the device body 101 becoming narrower and narrower.

[0055] Please refer to Figure 1A , as a typical embodiment, the device body 101 includes a display screen 10 and a middle frame 20. Among them, the display screen 10 is located on the front of the device body 101, and is used to display pictures and provide a man-machine interaction interface for users; the middle frame 20 is generally located between the display screen 10 and the rear shell of the electronic device, and is used to support the display screen 10 and carry various functional components inside the device body 101, such as a main board, a battery, a camera, a speaker, a microphone, various sensing units, etc. In a specific embodiment, the middle frame 20 includes a frame located on the periphery of the device body 101, and the frame may include multiple sides and carry a power button, a volume button or other function buttons. Among them, the fingerprint sensor 102 may be set on one of the sides of the frame and has a sensing area 108. In a specific embodiment, the fingerprint sensor 102 may specifically be a fingerprint recognition chip or a fingerprint module with a fingerprint recognition chip, which may be integrally set above the power button or volume button on the side of the frame, embedded in a predetermined area on the side of the frame, or attached to the inner surface of the side of the frame for users to input fingerprints to implement the side fingerprint function of the electronic device 100.

[0056] In some embodiments, the fingerprint sensor 102 may be disposed in a partial area or the entire area below the display screen 10, thereby forming an under-display fingerprint system. Alternatively, the fingerprint sensor 102 may be partially or fully integrated into the display screen 10 of the electronic device, thereby forming an in-display fingerprint system. Compared with the fingerprint sensor 102 disposed in an area outside the front display screen of the device body, the fingerprint sensor 102 disposed below the display screen of the electronic device 100 or integrated inside the display screen 10 can increase the screen-to-body ratio of the electronic device.

[0057] Please refer to Figure 1B , as another typical embodiment, different from the Figure 1A embodiment, the fingerprint sensor 102 is disposed below the display screen 10, that is, inside the display screen 10. The display screen 10 includes a cover glass 11, a touchpad 12, and a display panel 13 from top to bottom in sequence. The fingerprint sensor 102 may be disposed below the display panel 13. The fingerprint sensor 102 has a sensing area 108, and the area on the display screen 10 corresponding to the sensing area 108 is the fingerprint collection area. Usually, a visual prompt may be displayed in the fingerprint collection area on the display screen 10 to inform the user of the location of the fingerprint collection area.

[0058] The fingerprint sensor 102 may emit an acoustic wave signal and receive an echo signal reflected by the finger, and generate a fingerprint image according to the echo signal. Specifically, please refer to Figure 1B , the fingerprint sensor 102 may emit an acoustic wave signal to the display screen 10, so that the acoustic wave signal penetrates the cover glass 11, the touchpad 12, the display panel 13, etc. The signal may be reflected by the finger on the outer surface of the cover glass 11 to form an echo signal. The echo signal penetrates the cover glass 11, the touchpad 12, the display panel 13, etc. and reaches the sensing area 108 of the fingerprint sensor 102. The fingerprint sensor 102 generates a fingerprint image according to the echo signal. Figure 1A Similar to Figure 1B , details are not described herein again.

[0059] Please refer to Figure 1C, the fingerprint sensor 102 includes a sensing array 103, an output module 104, an interface module 105, and a driving module 106. Among them, the sensing array 103 is used to couple with the user's finger to collect the fingerprint information of the user's finger when the user presses the fingerprint sensor 102 for fingerprint input. Specifically, it includes a plurality of sensing units distributed in an array. The area where the sensing array 103 is located or its effective fingerprint collection area is the sensing area 108 of the fingerprint sensor 102. The sensing array 103 is also called a pixel array, and the sensing unit is also called a pixel unit. The driving module 106 and the output module 104 are respectively connected to the sensing array 103 and the interface module 105. Among them, the driving module 106 is used to drive the sensing array 103 to perform fingerprint scanning to collect the fingerprint information of the user's finger. The output module 104 is used to generate corresponding fingerprint data based on the fingerprint information collected by the sensing array 103, and output the fingerprint data to the control system 120 through the interface module 105. The interface module 105 can specifically be a Serial Peripheral Interface (SPI).

[0060] Continue to refer to Figure 1C , the control system 120 may include one or more general single-chip or multi-chip processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or combinations thereof. According to some examples, the control system 120 may include dedicated components for controlling the fingerprint sensor 102. In some implementations, the functionality of the control system 120 may be divided among one or more controllers or processors, such as between a dedicated sensor controller and an application processor of the electronic device. Refer to Figure 1C , the control system 120 may include an application processor 121 of the electronic device. The application processor 121 may specifically be a central processing unit (CPU) inside the electronic device 100 or other processing units or control units with processing capabilities, such as a microcontroller (MCU). It is connected to the interface module 105 and includes a fingerprint processing unit, mainly used to control the working state of the fingerprint sensor 102, process the fingerprint data output by the fingerprint sensor 102, and perform fingerprint template registration and fingerprint matching verification to determine whether the currently collected fingerprint image belongs to a legal fingerprint, and unlock the electronic device 100 or perform other fingerprint recognition-related functions according to the judgment result.

[0061] The embodiment of the present application provides an imaging target recognition method. This imaging target recognition method can be applied to an electronic device 100 as shown in Figure 1A , Figure 1B and Figure 1C as shown. Figure 2The flowchart of an imaging target recognition method provided by an embodiment of the present application is shown. This method recognizes the material of an imaging target based on multiple echo data corresponding to the imaging target respectively generated by a fingerprint sensor according to multiple preset configurations. This method can be applied to fingerprint anti-counterfeiting to better distinguish real fingers from fake fingers and improve the fingerprint anti-counterfeiting effect. This method can also be used for protective film recognition to identify the material of the protective film attached to the display screen. As Figure 2 shown, it specifically includes the following steps.

[0062] Step S201, obtain multiple echo data corresponding to the imaging target respectively generated by the fingerprint sensor according to multiple preset configurations.

[0063] In the embodiment of the present application, in the fingerprint anti-counterfeiting application, the imaging target is the input fingerprint, and the input fingerprint may be the fingerprint of a real finger or the fingerprint of a fake finger. The fake finger can be forged from materials such as resin and gelatin. The acoustic impedance of different materials is different. Please refer to Figure 3 the imaging model. The acoustic impedance of air is relatively small, about 0.004 MRayls, which is much smaller than the acoustic impedance of the display screen (screen cover plate) of 14 MRayls, and total reflection of ultrasonic waves occurs at the fingerprint valley line. The measured acoustic impedance of the fake fingerprint material and the real finger is in the same order of magnitude as the acoustic impedance of the display screen (screen cover plate), about 1 - 3 MRayl. Specifically, the acoustic impedance of the skin surface is about 1.68 MRayls, the acoustic impedance of resin is about 2.86 MRayls, and the typical acoustic impedance of gelatin is about 2.07 MRayls. There is partial reflection and partial transmission at the ridges of the true and false fingerprints. Assuming that the duty cycle of valleys and ridges in the image is 50%, then ideally, the difference in the AC part or (and) the signal amount of the image data mainly comes from the ridges and is only related to the material acoustic impedance. The actual imaging model is related not only to the acoustic impedance but also to factors such as good contact, fingerprint edge shape, and material sound speed and cannot be decoupled.

[0064] In the embodiment of the present application, in order to better distinguish real fingers from fake fingers and improve the fingerprint anti-counterfeiting effect, using the characteristic that the same material has different echo signals under different configurations, in the electronic device 100, the fingerprint sensor 102 can generate multiple echo data corresponding to the input fingerprint respectively according to multiple preset configurations. Further, it is possible to determine whether the input fingerprint is the fingerprint of a real finger based on the multiple echo data corresponding to the input fingerprint, and its possible implementation manners will be described later in this specification.

[0065] On the one hand, the same material has different echo signals under acoustic wave signals of different acoustic wave frequencies, and there is no direct or inverse proportional change law when the acoustic impedance changes, that is, the echo signals of materials with different acoustic impedances under acoustic wave signals of different acoustic wave frequencies do not have a direct or inverse proportional change law. In some implementations, the acoustic wave frequencies of any two of the multiple preset configurations are different. The acoustic wave frequency refers to the frequency of the acoustic wave signal emitted by the fingerprint sensor. On the other hand, different time of flights (TOFs) result in different received echo signals. Specifically, there are differences in the amplitude of the echo signals received in different time windows. Different echo data can be generated by controlling the time of flight, that is, receiving echo signals in different time windows and outputting corresponding echo data. In some implementations, the time of flights of different configurations can be different.

[0066] In some possible implementation manners, in the electronic device 100, the application processor 121 can control the fingerprint sensor 102 to respectively generate multiple echo data corresponding to the input fingerprint according to multiple preset configurations. Among them, the acoustic wave frequencies of any two preset configurations are different, and the time of flights of at least two configurations are different. Specifically, for each of the multiple preset configurations: the application processor 121 controls the fingerprint sensor 102 to emit an acoustic wave signal with the acoustic wave frequency of this preset configuration, and controls the fingerprint sensor 102 to receive the echo signal reflected by the input fingerprint according to the time of flight of this preset configuration and output the echo data corresponding to this preset configuration. The echo data can be a fingerprint image. The fingerprint sensor 102 can output the echo data corresponding to the preset configuration through the output module 105 and send the echo data to the application processor 121 through the interface module 105. The fingerprint processing unit included in the application processor 121 can process the echo data.

[0067] Typically, when the fingerprint sensor 102 receives the echo signal and outputs echo data, the configuration further includes the number of integration times. In some implementations, the number of integration times can be adaptively determined. Specifically, for each of the multiple preset configurations: The application processor 121 controls the fingerprint sensor 102 to emit an acoustic wave signal with the acoustic wave frequency of the preset configuration, controls the fingerprint sensor 102 to adaptively determine the number of integration times, and receives the echo signal reflected by the input fingerprint of the acoustic wave signal and outputs the echo data corresponding to the preset configuration according to the number of integration times and the flight time of the preset configuration. In some implementations, the number of integration times is defined in the preset configuration. By pre-defining the number of integration times in the preset configuration, the difference in echo data caused by the change of the number of integration times can be basically avoided. Specifically, for each of the multiple preset configurations: The application processor 121 controls the fingerprint sensor 102 to emit an acoustic wave signal with the acoustic wave frequency of the preset configuration, and controls the fingerprint sensor 102 to receive the echo signal reflected by the input fingerprint of the acoustic wave signal and output the echo data corresponding to the preset configuration according to the number of integration times and the flight time of the preset configuration.

[0068] Further, in some implementations, the application processor 121 can control the fingerprint sensor 102 to generate the above-mentioned multiple echo data in parallel. For example, the fingerprint sensor 102 can generate the echo data corresponding to configuration 0 according to configuration 0 and the echo data corresponding to configuration 1 according to configuration 1 substantially simultaneously. In some implementations, the application processor 121 can control the fingerprint sensor 102 to generate the above-mentioned multiple echo data serially. For example, the fingerprint sensor 102 generates the echo data corresponding to configuration 0 according to configuration 0, and then generates the echo data corresponding to configuration 1 according to configuration 1. In some implementations, the application processor 121 can control the fingerprint sensor 102 to generate the above-mentioned multiple echo data in a partially parallel and partially serial manner. For example, the fingerprint sensor 102 can generate the echo data corresponding to configuration 0 according to configuration 0 and the echo data corresponding to configuration 1 according to configuration 1 substantially simultaneously, and then generate the echo data corresponding to configuration 2 according to configuration 2. In specific implementation, the serial, parallel or serial combination with parallel manner can be selected according to the processing speed requirement to generate multiple echo data according to multiple configurations.

[0069] In the embodiments of the present application, the above-mentioned multiple preset configurations can be determined in advance and stored in the electronic device 100, for example, stored in the memory of the electronic device 100. In the embodiments of the present application, in the electronic device 100, the fingerprint sensor 102 can detect the contact of the user's finger or turn on the fingerprint collection function according to the instruction of the application processor 121 of the electronic device 100, and generate the echo data corresponding to the input fingerprint respectively according to the multiple pre-determined configurations stored in the memory.

[0070] In some possible implementation manners, such as Figure 4As shown, the method for determining the above-mentioned multiple preset configurations may include the following steps.

[0071] Step S401: Traverse the configurations in the configuration space. For each configuration, generate echo data of the test imaging target according to the configuration, and calculate multiple metrics of the echo data to obtain multiple evaluation metrics of the configuration. The test imaging target may include fingerprints of real fingers and fake fingers made of various materials.

[0072] The configuration space may be a three-dimensional space composed of acoustic wave frequency, flight time, and number of integrations. All points in this three-dimensional space are possible configurations. In a specific implementation, the acoustic wave frequency range may be divided into multiple acoustic wave frequency points at a certain step size. Similarly, the flight time range may be divided into multiple flight time points. Multiple configurations are combined by multiple acoustic wave frequency points, multiple flight time points, and multiple numbers of integrations. Each configuration is composed of acoustic wave frequency, flight time, and number of integrations. Specifically, the acoustic wave frequency range may be between 5Mhz and 25Mhz, the flight time range may be between 200ns and 2500ns, and the number of integrations is not less than 1.

[0073] In some possible implementation manners, the configuration includes acoustic wave frequency and flight time, and the fingerprint sensor adaptively determines the number of integrations. By adaptively determining the number of integrations, the integration voltage under different configurations can be adjusted to the same level, thereby ensuring that the dynamic range of the fingerprint sensor is maximized and will not saturate up and down. At this time, the configuration space is defined by the acoustic wave frequency range and the flight time range. In a specific implementation, the acoustic wave frequency range may be divided into multiple acoustic wave frequency points at a certain step size. Similarly, the flight time range may be divided into multiple flight time points. Multiple configurations are combined by multiple acoustic wave frequency points and multiple flight time points. Each configuration is composed of acoustic wave frequency and flight time.

[0074] Specifically, for each configuration, generating echo data corresponding to the test imaging target according to the configuration may include: emitting an acoustic wave signal with the acoustic wave frequency of the configuration by using the fingerprint sensor, adaptively determining the number of integrations by using the fingerprint sensor, and receiving the echo signal reflected by the test imaging target according to the number of integrations and the flight time of the configuration, and outputting the echo data corresponding to the configuration.

[0075] Furthermore, in the embodiments of the present application, the echo data may be an image, and the metrics of the echo signal may include but are not limited to: at least one of image signal amount, image signal-to-noise ratio, signal sensitivity, imaging sensitivity, amplitude of the alternating current signal in the image, amplitude of the alternating current signal after removing the background pattern in the image, image fluctuation information calculated in the spatial domain (i.e., spatial domain noise) of the image, and data amplitude of the image. The signal sensitivity is the image signal amount obtained by one integration, and the imaging sensitivity is the amount of alternating current signal increased by one integration.

[0076] Step S402: Select the multiple preset configurations from the configuration space according to multiple evaluation metrics of each configuration in the configuration space.

[0077] In some possible implementation manners, such as Figure 5 shown, selecting the multiple preset configurations from the configuration space according to multiple evaluation metrics of each configuration in the configuration space may include the following steps.

[0078] Execute Step S501 and Step S504 for each evaluation metric to determine the candidate configurations corresponding to each evaluation metric.

[0079] Step S501: Obtain a heat map of the evaluation metric by combining the acoustic wave frequency and flight time of each configuration. This heat map characterizes the size distribution of the evaluation metric among different configurations.

[0080] Please refer to Figure 6A . In an exemplary heat map, the horizontal direction is the flight time, the vertical direction is the acoustic wave frequency, the flight time range can be between 200 ns and 2500 ns, and the acoustic wave frequency range can be between 5 MHz and 25 MHz. Each pixel point in the heat map is a configuration composed of the flight time and the acoustic wave frequency. The value of each pixel point is the value of the evaluation metric. This heat map characterizes the size distribution of the evaluation metric among different configurations. In the heat map, the darker the color, the larger (or smaller) the corresponding evaluation metric. Specifically, if the evaluation metric is better when it is larger, the darker the color, the larger the evaluation metric; if the evaluation metric is better when it is smaller, the darker the color, the smaller the evaluation metric. Further, Figure 6A the "#" in it represents a point with missing data, or an overly large or overly small value.

[0081] Step S502: Project the heat map onto a three-dimensional space according to the evaluation metric value corresponding to each configuration to obtain a three-dimensional heat map.

[0082] Step S503: Determine the average Euclidean distance from the pixel point corresponding to each configuration in the three-dimensional heat map to other pixel points.

[0083] Please refer to Figure 6B shown. The three dimensions of the three-dimensional heat map are the acoustic wave frequency, the flight time, and the evaluation metric value respectively. The acoustic wave frequency, the flight time, and the evaluation metric value of each configuration correspond to a pixel point in the three-dimensional heat map. The pixel point corresponding to the i-th configuration can be expressed as (F i , T i , S i ), where Fi represents the acoustic wave frequency of the i-th configuration, T i represents the flight time of the i-th configuration, and S i represents the evaluation metric value of the i-th configuration for this evaluation metric. Thei The Euclidean distance L between the pixel points corresponding to the i-th configuration and the pixel points corresponding to the j-th configuration ij is calculated as follows:

[0084]

[0085] In the formula, (F i , T i , S i ) represents the pixel point corresponding to the i-th configuration, and (F j , T j , S j ) is the pixel point corresponding to the j-th configuration.

[0086] Step S504: Determine the candidate configurations corresponding to the evaluation index based on the average Euclidean distance corresponding to each configuration.

[0087] In the embodiments of the present application, considering that there may be deviations between the actual acoustic wave frequency and flight time when generating echo data according to the configuration and the acoustic wave frequency and flight time of the configuration, in order to avoid abnormal echo data caused by such deviations, through steps S501 to S504, the configurations adjacent to the candidate configurations also have evaluation index sizes close to those of the candidate configurations.

[0088] In some embodiments, before determining the average Euclidean distance from the pixel points corresponding to each configuration in the three-dimensional heat map to other pixel points, it further includes: identifying and removing the singular value points of the evaluation index in the heat map. Specifically, a window with a preset size is used to define the neighborhood of each pixel point in the heat map. For each pixel point, check how many pixel points are in its neighborhood. If there are fewer than the preset number of pixel points in the neighborhood of a pixel point, then this pixel point is considered a singular value point. In a specific implementation, the window size can be set to 3*3, and the preset number can be set to 3, that is, a 3*3 window is used to define the neighborhood of each pixel point in the heat map. For each pixel point, check how many pixel points are in its neighborhood. If there are fewer than 3 pixel points in the neighborhood of a pixel point, then this pixel point is considered a singular value point.

[0089] Step S505: Select the multiple preset configurations from the multiple candidate configurations corresponding to the multiple evaluation indexes.

[0090] In the embodiments of the present application, by comparing the results of different evaluation indexes, multiple preset configurations are selected according to project requirements. Exemplarily, for configuration 0, select the configuration with the largest image signal-to-noise ratio, for configuration 1, select the configuration with the largest signal sensitivity, and for configuration 2, select the configuration with the smallest spatial domain noise.

[0091] Step S202: Identify the material of the imaging target according to the multiple echo data.

[0092] In the embodiments of the present application, the material of the imaging target can be identified according to predefined rules, or an imaging target recognition model can be used to identify the material of the imaging target. The following will separately describe these two implementation manners.

[0093] Method 1

[0094] Figure 7 The flowchart showing the identification of the material of the imaging target according to predefined rules is as Figure 7 shown, and it may specifically include the following steps.

[0095] Step S701: Calculate one or more preset combined index values according to a plurality of echo data, where at least one combined index value is calculated by combining the indexes of at least two echo data.

[0096] Specifically, the echo data may be a fingerprint image, and the indexes of the echo data may include at least one of the following: image signal amount, image signal-to-noise ratio, signal sensitivity, imaging sensitivity, amplitude of the alternating current signal in the image, amplitude of the alternating current signal after removing the background pattern in the image, image fluctuation information calculated in the spatial domain of the image, and data amplitude of the image. The signal sensitivity is the image signal amount obtained by first integration, and the imaging sensitivity is the amount of alternating current signal increased by first integration.

[0097] For ease of understanding, an exemplary description of the combined index is given.

[0098] In some implementations, the index of the echo data may include the amplitude of the alternating current signal in the image. Table 1 shows the amplitudes of the alternating current signals in the images of Material 1, Material 2, Material 3, and Material 4 in Configuration 0 and Configuration 1. Among them, Configuration 0 has an ultrasonic emission frequency of 11.5 MHz, a flight time of 1300 ns, and 20 integrations, and Configuration 1 has an ultrasonic emission frequency of 14.5 MHz, a flight time of 1400 ns, and 30 integrations. Configuration 0 and Configuration 1 differ in terms of acoustic wave frequency, flight time, and number of integrations.

[0099] Table 1

[0100]

[0101] As shown in Table 1, for the amplitudes of the alternating current signals in the corresponding images under Configuration 0 and Configuration 1, the differences between Material 1, Material 2, Material 3 and Material 4 are not obvious, that is, the differences in the indexes of the echo data of a single configuration are not obvious. Subtracting the result corresponding to Configuration 0 from the result corresponding to Configuration 1, that is, the combined index is the difference in the amplitudes of the alternating current signals in the images under the two configurations, then the calculation result of Material 3 is negative, and the rest are all positive values greater than 10. It can be seen that based on the combined index of the difference in the amplitudes of the alternating current signals in the images under the two configurations, Material 3 can be well distinguished from Material 1, 2, and 4.

[0102] In some implementations, the metrics of the echo data may include signal sensitivity. Table 2 shows the signal sensitivities of Material 1, Material 2, Material 3, and Material 4 in Configuration 0, Configuration 1, and Configuration 2.

[0103] Table 2

[0104] Configuration 0 Configuration 1 Configuration 2 (Configuration 2 * Configuration 0) / Configuration 1 Material 1 1.00 1.00 2.00 0.2 Material 2 2.00 1.30 1.30 0.2 Material 3 1.30 1.90 4.38 0.3 Material 4 1.80 1.10 1.22 0.2

[0105] As shown in Table 2, for the signal sensitivities corresponding to Configuration 0, Configuration 1, and Configuration 1, Material 1, Material 2, and Material 3 are not significantly different from Material 4, that is, the differences in the metrics of the echo data for a single configuration are not obvious. The combined metric value calculation formula is: (Configuration 2 * Configuration 0) / Configuration 1. Then, the calculation result of Material 3 is quite different from that of Material 1, Material 2, and Material 4, and it can better distinguish Material 3 from Material 1, 2, and 4.

[0106] Step S702: Identify the material of the imaging target according to the one or more combined metric values and the metric value range corresponding to the material.

[0107] In a possible embodiment of the present application, the metric value ranges of multiple materials may be determined in advance. The metric value range of any material may include the range of one or more combined metric values. The combined metrics of different materials may be different. For example, the metric value range of the first material may include the first combined metric value range and the second combined metric value range, and the metric value range of the second material may include the third combined metric value range. The first material is different from the second material. If the combined metric value of the imaging target conforms to the metric value range corresponding to the material, then the imaging target is the material. This embodiment can identify which material among multiple materials the imaging target belongs to.

[0108] In a possible embodiment of the present application, the metric value range of the target material may be determined in advance. If the combined metric value of the imaging target conforms to the metric value range of the target material, then the imaging target is the target material. This embodiment can identify whether the imaging target is the target material.

[0109] In some possible implementation manners, the method may be applied to mis-touch identification. Specifically, it is determined whether the imaging target is human epidermal tissue according to multiple echo data. If the imaging target is not human epidermal tissue, then it can be determined as non-human contact. In some implementations, reference Figure 1BAs shown, the fingerprint sensor is disposed within or under the display screen. The fingerprint sensor and the display screen form an in-screen or under-screen fingerprint recognition system. The fingerprint sensor detects an object in contact with the display screen and generates multiple echo data corresponding to the contacted object according to multiple preset configurations. It determines whether the contacted object is human epidermal tissue based on the multiple echo data. If the contacted object is not human epidermal tissue, it can be determined that a non-human is in contact with the display screen. If the contacted object is human epidermal tissue, it can be determined that a human is in contact with the display screen. Further, it can be determined whether the contacted object is a fingerprint based on the echo data. If it is human epidermal tissue but not a fingerprint, it can be determined that it is a non-finger mis-touch. If it is human epidermal tissue and is a fingerprint, it is determined that a finger is in contact.

[0110] In some possible implementation manners, the method can be applied to the identification of the protective film of the display screen to identify the material of the protective film attached to the display screen. Specifically, in the application of protective film identification, the imaging target is the protective film attached to the display screen. By presetting the index value ranges of various film materials, it can be determined which index value range the combined index value of the protective film conforms to, and thus determine that the protective film is the corresponding film material. In practical applications, the materials of the protective film can include: polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET), and thermoplastic polyurethane (TPU). If the combined index value of the protective film conforms to the index value range of polypropylene, it is determined that the material of the protective film is polypropylene. If the combined index value of the protective film conforms to the index value range of polyvinyl chloride, it is determined that the material of the protective film is polyvinyl chloride. If the combined index value of the protective film conforms to the index value range of polyethylene terephthalate, it is determined that the material of the protective film is polyethylene terephthalate. And so on, it can be determined that the material of the protective film is thermoplastic polyurethane.

[0111] In some possible implementation manners, the method can be applied to fingerprint anti-counterfeiting to determine whether the input fingerprint is that of a real finger. Specifically, in the application of fingerprint anti-counterfeiting, the imaging target is the input fingerprint. It can be determined whether one or more combined index values conform to the index value range of a real finger, and based on the judgment result, it is determined whether the input fingerprint is that of a real finger. If one combined index value is used, when the combined index value conforms to the index value range of a real finger, it is determined that the input fingerprint is that of a real finger; otherwise, it is determined that the input fingerprint is that of a fake finger. If multiple combined index values are used, it can be determined that the input fingerprint is that of a real finger when any one of the combined index values conforms to the index value range of a real finger; otherwise, it is determined that the input fingerprint is that of a fake finger. It can also be determined that the input fingerprint is that of a real finger when all the multiple combined index values conform to the index value range of a real finger. In specific implementation, the judgment rules can be set according to needs.

[0112] In a specific implementation, echo data of a real finger and fake fingers made of various materials under multiple configurations can be analyzed. According to actual needs, indicators of the multiple echo data are jointly calculated to select a joint indicator value that can be used to distinguish between real fingers and fake fingers, and a range of real finger indicator values is defined, forming a predefined rule composed of the joint indicator value and the range of real finger indicator values. When performing fingerprint anti-counterfeiting, the joint indicator value corresponding to the input fingerprint is determined, and based on whether the joint indicator value of the input fingerprint conforms to the corresponding range of real finger indicator values, it is determined whether the input fingerprint is a fingerprint of a real finger.

[0113] The typical process of the above imaging target recognition method in fingerprint anti-counterfeiting applications can be summarized by Figure 8 the flowchart shown below. Figure 8 FIG. shows a flowchart of a fingerprint anti-counterfeiting method according to an exemplary embodiment of the present disclosure. As Figure 8 shown, the fingerprint anti-counterfeiting method of the embodiments of the present disclosure may include steps S801 to S805.

[0114] Step S801, obtain a plurality of preset configurations stored in advance.

[0115] In an embodiment of the present application, in the electronic device 100, the fingerprint sensor 102 may detect that a user's finger touches or, according to an instruction of the application processor 121 of the electronic device 100, turn on the fingerprint acquisition function and read a plurality of preset configurations determined in advance stored in the memory. The acoustic wave frequencies of any two of the plurality of preset configurations are different. Optionally, the flight times of at least two preset configurations are different. Optionally, the plurality of preset configurations further includes the number of integration times. In some implementations, the preset configuration does not include the number of integration times, and the fingerprint sensor adaptively determines the number of integration times. In the following description, it is assumed that the preset configuration includes the number of integration times for example.

[0116] The fingerprint sensor may generate echo data corresponding to the input fingerprint according to the plurality of preset configurations respectively. In the electronic device 100, the application processor 121 may control the fingerprint sensor 102 to generate echo data corresponding to the input fingerprint according to the plurality of preset configurations respectively. Specifically, steps S802 to S803 are executed for each of the plurality of preset configurations.

[0117] Step S802, the fingerprint sensor emits an acoustic wave signal with the acoustic wave frequency of the preset configuration.

[0118] Step S803, the fingerprint sensor receives the echo signal reflected by the input fingerprint from the acoustic wave signal according to the flight time and the number of integration times of the preset configuration and outputs the echo data corresponding to the preset configuration.

[0119] In an embodiment of the present application, if the preset configuration does not include the number of integrations, the fingerprint sensor can adaptively determine the number of integrations, and based on the determined number of integrations and the flight time of the preset configuration, receive the echo signal reflected by the input fingerprint through the sound wave signal and output the echo data corresponding to the preset configuration.

[0120] Step S804, calculate one or more preset combined index values according to multiple pieces of echo data respectively generated according to multiple preset configurations, where at least one combined index value is calculated by combining the indexes of at least two pieces of echo data.

[0121] In an embodiment of the present application, the echo data is a fingerprint image, and the indexes of the echo data may include but are not limited to: at least one of the image signal amount, image signal-to-noise ratio, signal sensitivity, imaging sensitivity, the AC signal amplitude in the image, the AC signal amplitude in the image after removing the background pattern, the image fluctuation information calculated for the image in the spatial domain, and the data amplitude of the image.

[0122] Step S805, determine whether the one or more combined index values conform to the index value range of a real finger, and determine whether the input fingerprint is the fingerprint of a real finger according to the judgment result.

[0123] Specifically, if one combined index value is used, when the combined index value conforms to the corresponding index value range of a real finger, it is determined that the input fingerprint is the fingerprint of a real finger. If multiple combined index values are used, it can be determined that the input fingerprint is the fingerprint of a real finger when any one of the combined index values conforms to the corresponding index value range of a real finger; it can also be determined that the input fingerprint is the fingerprint of a real finger when multiple combined index values all conform to the corresponding index value range of a real finger. In specific implementation, the judgment rule can be set according to needs.

[0124] Method 2

[0125] In Method 2, input multiple pieces of echo data into an imaging target recognition model, so that the imaging target recognition model outputs a model output indicating the material of the imaging target. As an implementation, the model output is a score indicating that the imaging target is the target material, used to identify whether the material of the imaging target is the target material. As another implementation, the model output is the material information of the imaging target. Specifically, the model output can be the probability distribution of multiple materials: for each material, the model output is a value between 0 and 1, and the sum of these values is 1, and this value represents the probability that the model believes the input data belongs to this material. In some cases, the model output can be the label of the most likely material, that is, the material with the highest probability.

[0126] In some possible embodiments, the imaging target recognition model is a neural network model, and the neural network model is trained to extract the joint features of multiple echo data respectively generated based on multiple preset configurations to obtain the model output. The neural network model may include a convolutional neural network model, and the convolutional neural network model may include a convolutional layer that performs a convolutional operation on the multiple echo data to extract the joint features of the multiple echo data.

[0127] In the application of protective film recognition, the model output may be the material information of the protective film. Specifically, multiple echo data of multiple film materials under multiple configurations can be collected, and their material information can be labeled as the corresponding materials. The network parameters of the neural network are trained using the collected data described above so that the neural network can distinguish different materials. Specifically, the materials of the protective film may include: polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET), thermoplastic polyurethane (TPU), etc.

[0128] In the application of fingerprint anti-counterfeiting, as one implementation, the model output is a score indicating that the input fingerprint is a fingerprint of a real finger. As another implementation, the model output is the material information of the input fingerprint. If the material information indicates that the input fingerprint is finger epidermal tissue, it can be determined that the input fingerprint is a fingerprint of a real finger; otherwise, it is determined that the input fingerprint is a fake fingerprint.

[0129] As one implementation, the neural network model is trained to receive multiple echo data corresponding to the input fingerprint, process the multiple echo data to output a score indicating that the input fingerprint is a fingerprint of a real finger. Specifically, multiple echo data of real fingers under multiple configurations can be collected and labeled as real fingers; multiple echo data of fake fingers made of multiple materials under multiple configurations can be collected and labeled as fake fingers. The network parameters of the neural network are trained using the collected data described above so that the neural network can distinguish real fingers and fake fingers.

[0130] As another implementation, the neural network model is trained to receive multiple echo data corresponding to the input fingerprint, process the multiple echo data to output the material information of the input fingerprint. Specifically, multiple echo data of real fingers under multiple configurations can be collected and their material information can be labeled as skin; multiple echo data of fake fingers made of multiple materials under multiple configurations can be collected and their material information can be labeled as the corresponding materials. The network parameters of the neural network are trained using the collected data described above so that the neural network can distinguish different materials.

[0131] The typical process of the above imaging target recognition method in the application of fingerprint anti-counterfeiting can be summarized by Figure 9 the flowchart shown. Figure 9 shows a flowchart of a fingerprint anti-counterfeiting method according to an exemplary embodiment of the present disclosure, as Figure 9As shown, the fingerprint anti-counterfeiting method of the embodiments of the present disclosure includes steps S901 to S905.

[0132] Step S901: Obtain a plurality of preset configurations stored in advance.

[0133] In an embodiment of the present application, in the electronic device 100, the fingerprint sensor 102 can detect the contact of the user's finger or activate the fingerprint acquisition function according to the instruction of the application processor 121 of the electronic device 100, and read a plurality of preset configurations determined in advance stored in the memory. The acoustic wave frequencies of any two of the plurality of preset configurations are different. Optionally, the time of flight of at least two preset configurations is different. Optionally, the plurality of preset configurations further includes the number of integration times. In some implementations, the preset configuration does not include the number of integration times, and the fingerprint sensor adaptively determines the number of integration times. In the following description, it is assumed that the preset configuration does not include the number of integration times for illustration purposes.

[0134] The fingerprint sensor can generate echo data corresponding to the input fingerprint according to the plurality of preset configurations respectively. In the electronic device 100, the application processor 121 can control the fingerprint sensor 102 to generate echo data corresponding to the input fingerprint according to the plurality of preset configurations respectively. Specifically, steps S902 to S903 are executed for each of the plurality of preset configurations.

[0135] Step S902: The fingerprint sensor emits an acoustic wave signal with the acoustic wave frequency of the preset configuration.

[0136] Step S903: The fingerprint sensor adaptively determines the number of integration times, and according to the number of integration times and the time of flight of the preset configuration, receives the echo signal reflected by the input fingerprint of the acoustic wave signal and outputs the echo data corresponding to the preset configuration.

[0137] Step S904: Input the plurality of echo data into the fingerprint anti-counterfeiting model so that the fingerprint anti-counterfeiting model outputs a model output indicating whether the input fingerprint is a fingerprint of a real finger.

[0138] Step S905: Judge whether the input fingerprint is a fingerprint of a real finger according to the model output.

[0139] If the model output is a score indicating that the input fingerprint is a fingerprint of a real finger, the score can be compared with a preset threshold. If the score is greater than the preset threshold, the input fingerprint is a fingerprint of a real finger; otherwise, the input fingerprint is not a fingerprint of a real finger, that is, the input fingerprint is a false fingerprint.

[0140] If the model output is the material information of the input fingerprint, judge whether the material of the input fingerprint is skin. If the material information indicates that the input fingerprint is skin, the input fingerprint is a fingerprint of a real finger; otherwise, it is determined that the input fingerprint is a false fingerprint.

[0141] The typical process of the above imaging target recognition method in the application of protective film recognition can be summarized by Figure 10 the flowchart shown in Figure 10 FIG. 4 shows a flowchart of a method for recognizing a protective film according to an exemplary embodiment of the present disclosure. As Figure 10 shown, the method for recognizing a protective film according to the embodiment of the present disclosure includes steps S1001 to S1004.

[0142] Step S1001: Obtain a plurality of preset configurations stored in advance.

[0143] In an embodiment of the present application, in the electronic device 100, the fingerprint sensor 102 can turn on the fingerprint collection function according to the instruction of the application processor 121 of the electronic device 100 and read a plurality of preset configurations determined in advance stored in the memory. The acoustic wave frequencies of any two of the plurality of preset configurations are different. Optionally, the time of flight of at least two of the preset configurations is different. Optionally, the plurality of preset configurations further includes the number of integration times. In some implementations, the preset configuration does not include the number of integration times, and the fingerprint sensor adaptively determines the number of integration times. In the following description, an example in which the preset configuration does not include the number of integration times is used for illustration.

[0144] The fingerprint sensor can generate echo data corresponding to the imaging target according to the plurality of preset configurations respectively. In an embodiment of the present application, in the electronic device 100, referring to Figure 1B FIG. 5, the protective film is pasted on the surface of the display screen 10. The fingerprint sensor 102 sends an acoustic wave signal, and the acoustic wave signal forms an echo signal after being reflected by the protective film. The fingerprint sensor 102 receives the echo data and outputs the echo data. In the electronic device 100, the application processor 121 can control the fingerprint sensor 102 to generate echo data corresponding to the protective film according to the plurality of preset configurations respectively. Specifically, steps S1002 to S1003 are executed for each of the plurality of preset configurations.

[0145] Step S1002: The fingerprint sensor emits an acoustic wave signal with the acoustic wave frequency of the preset configuration.

[0146] Step S1003: The fingerprint sensor adaptively determines the number of integration times, and receives the echo signal of the acoustic wave signal reflected by the protective film according to the number of integration times and the time of flight of the preset configuration, and outputs the echo data corresponding to the preset configuration.

[0147] Step S1004: Input the plurality of echo data into the imaging target recognition model so that the imaging target recognition model outputs a model output indicating the material of the protective film.

[0148] The specific model output can be the probability distribution of multiple materials: for each material, the model outputs a value between 0 and 1, and the sum of these values is 1. This value represents the probability that the model believes the input data belongs to this material. In some cases, the model output can be the label of the most likely material, that is, the material with the highest probability.

[0149] In another embodiment, the index value ranges of multiple film materials can be preset. When identifying the protective film, one or more combined index values preset are calculated based on multiple echo data, and the index value range that the combined index value of the protective film conforms to is judged to determine that the protective film is the corresponding film material. In practical applications, the materials of the protective film can include: polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET), thermoplastic polyurethane (TPU). If the combined index value of the protective film conforms to the index value range of polypropylene, it is determined that the material of the protective film is polypropylene; if the combined index value of the protective film conforms to the index value range of polyvinyl chloride, it is determined that the material of the protective film is polyvinyl chloride; if the combined index value of the protective film conforms to the index value range of polyethylene terephthalate, it is determined that the material of the protective film is polyethylene terephthalate; and so on, it can be determined that the material of the protective film is thermoplastic polyurethane.

[0150] The embodiment of the present application also provides a fingerprint processing device, such as Figure 11 shown, the device includes: a fingerprint sensor 1110 and a processing unit 1120. The fingerprint sensor 1110 is used to: generate multiple echo data corresponding to the input fingerprint according to multiple configurations respectively. The processing unit 1120 is used to execute the imaging target recognition method of the embodiment of the present application. The fingerprint processing device can specifically be Figure 1C the electronic device 100 shown. Among them, the fingerprint sensor 1110 can specifically be Figure 1C the fingerprint sensor 102 shown, and the processing unit 1020 can specifically be Figure 1C the application processor 121 shown (such as a central processing unit CPU) for executing the main steps of the methods in the above various embodiments. In other alternative embodiments, the processing unit 1120 can also be implemented by other processing units or control units with image processing capabilities (such as a microcontroller MCU).

[0151] The embodiment of the present application also provides an electronic device 100, which can further include: an application processor 121; and a memory for storing a program, where the program includes instructions, and when the instructions are executed by the application processor 121, the application processor 121 is caused to execute the methods in the above embodiments, such as Figure 2 、 Figure 4 、 Figure 5 、 Figures 7 to 10 the methods shown.

[0152] Embodiments of the present application also provide a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause the application processor 121 of the electronic device 100 to execute the method of the above embodiments, for example Figure 2 , Figure 4 , Figure 5 , Figures 7 to 10 the methods shown.

[0153] The above are only the preferred embodiments of the present application and do not impose any form of limitation on the present application. Although the present application has been disclosed above with the preferred embodiments, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present application. However, as long as it does not depart from the content of the technical solution of the present application, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application still fall within the scope of the technical solution of the present application.

Claims

1. An imaging target recognition method, the method is applied to an electronic device, characterized in that: The method comprises: Acquire a plurality of echo data corresponding to the imaging target respectively generated by the fingerprint sensor according to a plurality of preset configurations; The material of the imaging target is identified based on the plurality of echo data.

2. The imaging target recognition method according to claim 1, characterized in that: The identifying the material of the imaging target according to the plurality of echo data comprises: Calculating one or more preset joint index values ​​according to the plurality of echo data, wherein at least one of the joint index values ​​is calculated based on the joint indexes of at least two of the echo data; The material of the imaging target is identified based on the one or more joint index values ​​and the index value range corresponding to the material.

3. The imaging target recognition method according to claim 2, characterized in that: Identifying the material of the imaging target according to the one or more joint index values ​​includes: It is determined whether the one or more combined index values ​​are in accordance with the index value range of the target material, and it is determined whether the material of the imaging target is the target material according to the result of the determination.

4. The imaging target recognition method according to claim 2, characterized in that: The echo data is an image, and the index includes at least one of: image signal quantity, image signal-to-noise ratio, signal sensitivity, imaging sensitivity, AC signal amplitude in the image, AC signal amplitude after removing background patterns from the image, image fluctuation information calculated in the spatial domain, and image data amplitude; The signal sensitivity is the amount of image signal obtained by one integration, and the imaging sensitivity is the amount of AC signal increased by one integration.

5. The imaging target recognition method according to claim 1, characterized in that: The identifying the material of the imaging target according to the plurality of echo data comprises: The plurality of echo data are input into an imaging target recognition model so that the imaging target recognition model outputs a model output indicating a material of the imaging target.

6. The imaging target recognition method according to claim 5, characterized in that: The model output is a score indicating whether the imaging object is a target material; or The model output is material information of the imaging target.

7. The imaging target recognition method according to claim 5, characterized in that: The imaging target recognition model is a neural network model, and the neural network model is trained to extract joint features of a plurality of echo data respectively generated based on the plurality of preset configurations to obtain the model output.

8. The imaging target recognition method according to any one of claims 1 to 7, characterized in that: The sound wave frequencies and / or flight times of any two preset configurations among the plurality of preset configurations are different.

9. The imaging target recognition method according to any one of claims 1 to 7, characterized in that: Before acquiring a plurality of echo signals corresponding to the imaging target respectively generated by the fingerprint sensor according to a plurality of preset configurations, the method further includes: For each preset configuration in the plurality of preset configurations: Using the fingerprint sensor to transmit a sound wave signal of the preset sound wave frequency; The fingerprint sensor is used to receive the echo signal of the acoustic wave signal reflected by the imaging target according to the flight time of the preset configuration and output the echo data corresponding to the preset configuration.

10. The imaging target recognition method according to any one of claims 1 to 7, characterized in that: Also includes: Traversing the configurations in the configuration space, for each configuration: generating echo data of a test imaging target according to the configuration, and calculating a plurality of indices of the echo data to obtain a plurality of evaluation indices of the configuration; The plurality of preset configurations are selected from the configuration space according to the plurality of evaluation indicators of each configuration in the configuration space.

11. The imaging target recognition method according to claim 10, characterized in that: The configuration space is defined by a range of acoustic wave frequencies and a range of flight times, and the configuration includes acoustic wave frequencies and flight times.

12. The imaging target recognition method according to claim 11, characterized in that: The step of generating echo data of a test imaging target according to the configuration comprises: Using the fingerprint sensor to transmit a sound wave signal of the configured sound wave frequency; The fingerprint sensor is used to adaptively determine the number of integration times, and according to the number of integration times and the configured flight time, an echo signal of the acoustic wave signal reflected by the test imaging target is received and echo data corresponding to the configuration is output.

13. The imaging target recognition method according to claim 11, characterized in that: The plurality of preset configurations are selected from the configuration space according to the evaluation index of each configuration in the configuration space, comprising: For each evaluation index: a heat map of the evaluation index is obtained according to the combination of the sound wave frequency and the flight time of each configuration, wherein the heat map represents the size distribution of the evaluation index in different configurations; the heat map is projected into a three-dimensional space according to the evaluation index value corresponding to each configuration to obtain a three-dimensional heat map; the average Euclidean distance from the pixel point corresponding to each configuration to other pixel points in the three-dimensional heat map is determined, and the candidate configuration corresponding to the evaluation index is determined based on the average Euclidean distance corresponding to each configuration; The plurality of preset configurations are obtained by selecting from a plurality of candidate configurations corresponding to the plurality of evaluation indicators.

14. The imaging target recognition method according to claim 13, characterized in that: Before determining the average Euclidean distance from the pixel point corresponding to each configuration in the three-dimensional heat map to other pixel points, the method further includes: Identify and remove singular value points of the evaluation index in the heat map.

15. The imaging target recognition method according to any one of claims 1 to 14, characterized in that: The identifying the material of the imaging target according to the multiple echo data includes: judging whether the imaging target is human epidermal tissue according to the multiple echo data.

16. The imaging target recognition method according to claim 15, characterized in that: The electronic device includes a display screen, and the fingerprint sensor is located inside or under the display screen. The method also includes: identifying whether the imaging target is a fingerprint based on the multiple echo data; if the imaging target is not a fingerprint but human epidermal tissue, determining that the display screen is not accidentally touched by a finger.

17. The imaging target recognition method according to any one of claims 1 to 14, characterized in that: The method is applied to fingerprint anti-counterfeiting, the imaging target is an input fingerprint, and the input fingerprint includes a fingerprint of a real finger and a fingerprint of a fake finger.

18. The imaging target recognition method according to any one of claims 1 to 14, characterized in that: The electronic device comprises a display screen, the method is applied to identifying the material of a protective film, and the imaging target is the protective film attached to the surface of the display screen.

19. A fingerprint processing device, characterized in that: The device comprises: A fingerprint sensor, configured to generate a plurality of echo data corresponding to an imaging target according to a plurality of configurations; A processing unit, used to execute the imaging target recognition method described in any one of 1-18.

20. An electronic device, characterized in that: The electronic device comprises: the fingerprint processing device according to claim 19.

21. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1-18.

Images