Fingerprint identification method, device, electronic device and storage medium

Abstract

The present application provides a fingerprint recognition method, device, electronic device and storage medium, which respectively obtain first fingerprint data corresponding to an irregular light spot and second fingerprint data corresponding to a normal light spot during a single press, and then obtain bright area data and dark area data corresponding to the first fingerprint data and fingerprint feature information related to the second fingerprint data. Since the bright area data and dark area data corresponding to the first fingerprint data can be used to characterize the difference between a real finger and a 2.5D fake fingerprint, that is, the difference in the amount of transmitted light generated by the real finger and the 2.5D fake fingerprint, the authenticity of the fingerprint to be detected can be effectively determined based on the fingerprint feature information, thereby improving the accuracy of the fingerprint recognition result for the 2.5D fake fingerprint.

Description

Technical Field

[0001] The present application relates to the field of fingerprint recognition technology, and in particular to a fingerprint recognition method, device, electronic device and storage medium. Background Art

[0002] With the widespread application of optical fingerprint recognition technology in terminal devices, users have increasingly higher requirements for the security of fingerprint recognition.

[0003] Existing fingerprint recognition solutions can better identify real fingerprints and forged flat fake fingerprints (fingerprints without three-dimensional fingerprint features, also known as 2D fake fingerprints). However, since fake fingerprints (also known as 2.5D fake fingerprints) made from extracted user fingerprints through simple processes such as etching circuit boards have 3D depth features, existing fingerprint recognition solutions have poor interception effects on such fake fingerprints, seriously affecting the information security of end users.

[0004] Therefore, how to identify real fingerprints and 2.5D fake fingerprints to improve the security of fingerprint recognition is an urgent problem to be solved. Summary of the invention

[0005] The present application provides a fingerprint recognition method, device, electronic device and storage medium to solve the problems existing in the prior art.

[0006] In a first aspect, the present application provides a fingerprint recognition method, comprising:

[0007] When a pressing operation of a finger in the pressing area is detected, first fingerprint data and second fingerprint data corresponding to the fingerprint to be detected are respectively obtained, wherein the first fingerprint data is data collected when a light-emitting unit corresponding to the pressing area is partially illuminated, and the second fingerprint data is data collected when the light-emitting unit corresponding to the pressing area is fully illuminated;

[0008] Determine fingerprint feature information according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data, wherein the bright area data is data in an area where the light-emitting unit in the light-emitting state is located, and the dark area data is data in an area where the light-emitting unit in the light-off state is located;

[0009] The authenticity of the fingerprint to be detected is determined according to the fingerprint feature information.

[0010] In some embodiments, determining fingerprint feature information according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data includes:

[0011] Determine calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data, and the second fingerprint data;

[0012] Fingerprint feature information is determined based on the calibration data.

[0013] In some embodiments, the calibration data includes first calibration data corresponding to the first fingerprint data and second calibration data corresponding to the second fingerprint data;

[0014] The step of determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data includes:

[0015] Regularizing the bright area data and the dark area data corresponding to the first fingerprint data by using a first type of preset data set to obtain the first calibration data, wherein the first type of preset data set is a data set obtained when the light-emitting unit partially emits light and used to calibrate the fingerprint data collected when the light-emitting unit partially emits light during the pressing process;

[0016] The second fingerprint data is regularized by a second type of preset data set to obtain the second calibration data, wherein the first type of preset data set is a data set obtained when the light-emitting units are fully illuminated and used to calibrate the fingerprint data collected when the light-emitting units are fully illuminated during the pressing process.

[0017] In some embodiments, the fingerprint feature information includes the repetition of fingerprint data and the size relationship between fingerprint data;

[0018] Determining fingerprint feature information according to the calibration data includes:

[0019] Selecting first sub-data from the first calibration data, wherein the first sub-data is row data including bright area data and dark area data, and / or the first sub-data is column data including bright area data and dark area data;

[0020] selecting second sub-data from the second calibration data, wherein a position of the second sub-data in the second fingerprint data is the same as a position of the first sub-data in the first fingerprint data;

[0021] Determine the repetition degree between the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data;

[0022] It is determined that the dark area data in the first sub-data is smaller than the data amount of the fingerprint data at the corresponding position in the second sub-data, and the data amount is used to characterize the size relationship between the first sub-data and the second sub-data.

[0023] In some embodiments, determining the authenticity of the fingerprint to be detected according to the fingerprint feature information includes:

[0024] Determining whether both the first sub-data and the second sub-data are valid according to the repetition degree of the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data;

[0025] When it is determined that both the first sub-data and the second sub-data are valid, if the dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data by a first preset threshold, the fingerprint to be detected is determined to be a true fingerprint.

[0026] In some embodiments, determining whether both the first sub-data and the second sub-data are valid according to the repetition degree of the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data includes:

[0027] If the amount of bright area data in the first sub-data is equal to the fingerprint data at the corresponding position in the second sub-data and reaches a second preset threshold, it is determined that both the first sub-data and the second sub-data are valid.

[0028] In some embodiments, the fingerprint feature information includes the size relationship of the fingerprint data, the mean value of the fingerprint data, the difference of the mean value of the fingerprint data, and the difference between the dark area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data;

[0029] Determining fingerprint feature information according to the calibration data includes:

[0030] Selecting first sub-data from the first calibration data, the first sub-data being row data including the bright area data and the dark area data, and / or the first sub-data being column data including the bright area data and the dark area data;

[0031] selecting second sub-data from the second calibration data, wherein a position of the second sub-data in the second fingerprint data is the same as a position of the first sub-data in the first fingerprint data;

[0032] Determine that the dark area data in the first sub-data is smaller than the data amount of the fingerprint data at the corresponding position in the second sub-data;

[0033] Determine a first data mean of dark area data in the first sub-data, a second data mean of data at a corresponding position in the second sub-data, and a difference between the first data mean and the second data mean;

[0034] Determine the difference between each dark area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data, and determine the sum of the absolute values ​​of each difference, wherein the sum is used to characterize the difference between the first sub-data and the second sub-data.

[0035] In some embodiments, the calibration data includes third calibration data corresponding to the first fingerprint data;

[0036] The step of determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data includes:

[0037] Selecting third sub-data from the bright area data corresponding to the first fingerprint data, and determining a third data mean value of the third sub-data;

[0038] Selecting fourth sub-data from the second fingerprint data, determining a fourth data mean of the fourth sub-data, wherein a position of the fourth sub-data in the second fingerprint data corresponds to the same position of the third sub-data in the first fingerprint data;

[0039] Determining a ratio of the third data mean to the fourth data mean;

[0040] Based on the bright area data and the dark area data of the first fingerprint data, the second fingerprint data and the ratio, regularization processing is performed on the first fingerprint data to obtain the third calibration data.

[0041] In some embodiments, the fingerprint feature information includes quantitative feature information;

[0042] Determining fingerprint feature information according to the calibration data includes:

[0043] Determine quantitative feature information of the bright area data and / or the dark area data corresponding to the third calibration data, wherein the quantitative feature information includes at least one of a data mean, a data variance, a data standard deviation, and a gradient value.

[0044] In some embodiments, the calibration data further includes third calibration data corresponding to the first fingerprint data;

[0045] The determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data further includes:

[0046] Selecting third sub-data from the bright area data corresponding to the first fingerprint data, and determining a third data mean value of the third sub-data;

[0047] Selecting fourth sub-data from the second fingerprint data, determining a fourth data mean of the fourth sub-data, wherein a position of the fourth sub-data in the second fingerprint data corresponds to the same position of the third sub-data in the first fingerprint data;

[0048] Determining a ratio of the third data mean to the fourth data mean;

[0049] Based on the bright area data and the dark area data of the first fingerprint data, the second fingerprint data and the ratio, regularization processing is performed on the first fingerprint data to obtain the third calibration data.

[0050] In some embodiments, the fingerprint feature information also includes quantitative feature information;

[0051] The determining fingerprint feature information according to the calibration data further includes:

[0052] Determine quantitative feature information of the bright area data and / or the dark area data corresponding to the third calibration data, wherein the quantitative feature information includes at least one of a data mean, a data variance, a data standard deviation, and a gradient value.

[0053] In some embodiments, determining the authenticity of the fingerprint to be detected according to the fingerprint feature information includes:

[0054] Inputting the fingerprint feature information into a pre-trained decision tree model to obtain a score indicating that the fingerprint to be detected is a true fingerprint;

[0055] The authenticity of the fingerprint to be detected is determined based on a comparison result between the score and a preset fingerprint threshold.

[0056] In a second aspect, the present application provides a fingerprint recognition device, comprising:

[0057] A fingerprint data acquisition module, used to respectively acquire first fingerprint data and second fingerprint data corresponding to the fingerprint to be detected when a pressing operation of a finger in a pressing area is detected, wherein the first fingerprint data is data collected when a light-emitting unit corresponding to the pressing area is partially illuminated, and the second fingerprint data is data collected when the light-emitting unit corresponding to the pressing area is fully illuminated;

[0058] a feature information determination module, configured to determine fingerprint feature information according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data, wherein the bright area data is data in an area where the light-emitting unit in the light-emitting state is located, and the dark area data is data in an area where the light-emitting unit in the light-off state is located;

[0059] The fingerprint authenticity determination module is used to determine the authenticity of the fingerprint to be detected based on the fingerprint feature information.

[0060] In a third aspect, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned fingerprint recognition method when executing the program.

[0061] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned fingerprint recognition method.

[0062] The fingerprint recognition method, device, electronic device and storage medium provided by the present application respectively obtain the first fingerprint data corresponding to the irregular light spot and the second fingerprint data corresponding to the normal light spot during a single pressing process, and then obtain the bright area data and dark area data corresponding to the first fingerprint data and fingerprint feature information related to the second fingerprint data. Since the bright area data and dark area data corresponding to the first fingerprint data can be used to characterize the difference between the real finger and the 2.5D fake fingerprint, that is, the difference in the amount of transmitted light generated by the real finger and the 2.5D fake fingerprint, the authenticity of the fingerprint to be detected can be effectively determined based on the fingerprint feature information, thereby improving the accuracy of the fingerprint recognition result for the 2.5D fake fingerprint. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0064] Figure 1 A cross-sectional schematic diagram of an electronic device to which the embodiments of the present application may be applied;

[0065] Figure 2 This is a schematic diagram of the light-emitting unit corresponding to the fingerprint collection area in the embodiment of the present application fully emitting light or partially emitting light;

[0066] Figure 3 This is a schematic diagram of a real finger pressing the screen in an embodiment of the present application;

[0067] Figure 4 This is a schematic diagram of a 2.5D fake fingerprint pressing the screen in an embodiment of the present application;

[0068] Figure 5 A schematic diagram of a fingerprint recognition method provided in an embodiment of the present application;

[0069] Figure 6 A schematic diagram of obtaining a first preset data set provided in an embodiment of the present application;

[0070] Figure 7 A schematic diagram of obtaining a second preset data set provided in an embodiment of the present application;

[0071] Figure 8 A schematic diagram of selecting first sub-data from first calibration data according to an embodiment of the present application;

[0072] Fig. 9A schematic diagram of selecting second sub-data from second calibration data according to an embodiment of the present application;

[0073] Fig.10 This is an example diagram of fingerprint authenticity identification in an embodiment of the present application;

[0074] Fig.11 Another example diagram of fingerprint authenticity identification in the embodiment of the present application;

[0075] Fig.12 Another example diagram of fingerprint authenticity identification in the embodiment of the present application;

[0076] Fig.13 Another example diagram of fingerprint authenticity identification in the embodiment of the present application;

[0077] Fig.14 This is a schematic diagram of the first fingerprint data in an embodiment of the present application;

[0078] Fig.15 This is a schematic diagram of the second fingerprint data in an embodiment of the present application;

[0079] Fig.16 Another schematic diagram of selecting the sixth sub-data and the seventh sub-data in an embodiment of the present application;

[0080] Fig.17 Another schematic diagram of selecting the sixth sub-data and the seventh sub-data in an embodiment of the present application;

[0081] Fig.18 A schematic diagram of a fingerprint recognition device provided in an embodiment of the present application.

[0082] The above drawings show clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "an" and "the" used in the embodiments of the present application are also intended to include plural forms, unless the context clearly indicates other meanings.

[0085] It should be understood that the term "and / or" used in this article is only a way to describe the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the characters "three" in this article generally indicate that the associated objects before and after are in an "or" relationship.

[0086] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such a product or system. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the product or system including the elements.

[0087] The 2.5D fake fingerprint made by the prior art is a type of fake fingerprint that is as easy to make as a 2D fake fingerprint. First, the extracted fingerprint is made into a fake fingerprint mold using a simple process (printing on film paper with an ordinary printer, etching circuit board process, UV printing, etc.), and then it is printed with fake fingerprint making materials (such as white glue, wood glue, black glue, silicone, seam beautifier, paint, gelatin, etc.). After it is formed, it is removed to obtain a 2.5D fake fingerprint with 3D stereoscopic features.

[0088] In the lens-type fingerprint recognition system, the existing fingerprint recognition scheme can better identify real fingerprints and forged 2D fake fingerprints, but the interception effect of 2.5D fake fingerprints is poor, which seriously affects the information security of terminal users.

[0089] The fingerprint recognition method, device, electronic device, storage medium and product provided in this application are intended to solve the above technical problems in the prior art.

[0090] The technical solution provided in the embodiments of the present application can be applied to various electronic devices. For example, smart phones, tablet computers and other mobile terminals or other electronic devices with display screens and fingerprint recognition devices. More specifically, in the above-mentioned electronic devices, the fingerprint recognition device can be specifically an optical fingerprint device, which can be set in a local area or the entire area below the display screen, thereby forming an under-display or under-screen optical fingerprint system. Specifically, in the electronic device, the fingerprint recognition device receives light returned from the top surface of the display screen of the electronic device, and this returned light carries information of an object (such as a finger) in contact with the top surface of the display screen, thereby acquiring the fingerprint information of the finger by collecting and detecting this returned light.

[0091] Figure 1 Schematic diagram of a cross-section of an electronic device to which the present application embodiment can be applied. Figure 1 As shown, the electronic device includes a display screen 12 and a fingerprint recognition device 13, wherein the display screen 12 includes an upper cover plate 121, a light-emitting layer 122 and a lower cover plate 123. Specifically, depending on the light-emitting layer, the display screen 12 can be a display screen with a self-luminous display unit or a non-self-luminous display screen.

[0092] The display screen 12 is a display screen having a self-luminous display unit, for example, Figure 1 As shown, the display screen 12 may be an organic light-emitting diode (OLED) display screen, however, the present application is not limited thereto, for example, a micro-light-emitting diode (Micro-LED) may also be used. When an OLED display screen is used, the fingerprint recognition device 13 may use the OLED light source of the display screen 12 corresponding to the fingerprint collection area as an excitation light source for fingerprint detection. When the object 11 is pressed on the fingerprint collection area of ​​the display screen 12, the light source (including the light-emitting unit) at the corresponding position in the display screen 12 emits a light beam to the object 11 above the fingerprint collection area, and the light beam is reflected on the surface where the object 11 contacts the screen to form reflected light, and the light beam passes through part of the object 11 to form transmitted light. After the reflected light and the transmitted light of different intensities pass through the optical components, they are received by the fingerprint recognition device 13 and converted into corresponding electrical signals, namely fingerprint detection signals. Based on the fingerprint detection signal, fingerprint data can be obtained for realizing the fingerprint recognition function in the electronic device.

[0093] In addition, when the display screen 12 is a non-self-luminous display screen, such as a liquid crystal display screen, the fingerprint recognition device 13 needs to use a built-in light source or an external light source as an excitation light source to provide an optical signal for fingerprint detection. The fingerprint detection principle when the built-in light source or the external light source is used as the excitation light source is the same as the fingerprint detection principle when the OLED display screen is used as mentioned above, and will not be repeated here.

[0094] refer to Figure 1 , the fingerprint recognition device 13, specifically an optical fingerprint recognition device, can be arranged below the display screen 12, and can include a multi-light path structure 131 and an optical detection component 132. Among them, the multi-light path structure 131 can be arranged above the optical detection component 132, and is mainly used to guide the light signal reflected or scattered from the finger to the optical detection component for optical detection by the optical detection component 132. The optical detection component 132 includes a photosensitive array and a reading circuit and other auxiliary circuits electrically connected to the photosensitive array. The photosensitive array may include a plurality of photosensitive units distributed in an array, which may also be referred to as pixel units or photosensitive pixels. The photosensitive array is mainly used to detect the received light signal so as to generate fingerprint data through a reading circuit electrically connected thereto.

[0095] In the embodiment of the present application, when collecting fingerprint data of the object 11, the light-emitting units corresponding to the fingerprint collection area pressed by the object 11 may emit light in whole or in part. Among them, full illumination means that all light-emitting units in the fingerprint collection area emit light beams, thereby forming a complete light spot, and partial illumination means that a part of the light-emitting units in the fingerprint collection area emits light beams, while another part of the light-emitting units do not emit light beams, thereby failing to form a complete light spot, which is called an irregular light spot.

[0096] For example, Figure 2 Schematic diagram of the light-emitting unit corresponding to the fingerprint collection area in the embodiment of the present application fully or partially emitting light, such as Figure 2 As shown, the fingerprint collection area can be at position 12a of the display screen 12. At this time, the light-emitting units corresponding to the fingerprint collection area all emit light (white indicates light emission, and the light-emitting area is called a bright area), thereby forming a complete light spot; the fingerprint collection area can also be at position 12b of the display screen 12. At this time, the light-emitting units corresponding to the fingerprint collection area partially emit light (black indicates no light emission, and the no light emission area is called a dark area), thereby forming an irregular light spot; in addition, as shown in 12c, 12d, 12e, and 12f, the position of the non-luminous light-emitting unit and the shape formed can change, and the present application does not make specific limitations on this.

[0097] Figure 3 This is a schematic diagram of a real finger pressing the screen in an embodiment of the present application. Figure 4Schematic diagram of a 2.5D fake fingerprint pressing the screen in an embodiment of the present application. Figure 3 as well as Figure 4 As shown in FIG. 1 , when the light-emitting unit corresponding to the fingerprint collection area partially emits light to form an irregular light spot, the bright area in the figure represents the area where the light-emitting unit emits light, and the dark area represents the area where the light-emitting unit does not emit light. In addition, Figure 4 In the process, since common fake fingerprint materials themselves usually cannot trigger the fingerprint recognition function of electronic devices, other materials or objects with triggering image collection functions, such as real fingers, need to be placed on top of the fake fingerprint materials to trigger the fingerprint recognition function, resulting in Figure 4 The real finger and fake fingerprint material together form a thicker thickness.

[0098] refer to Figure 3 as well as Figure 4 ,In the irregular light spot, the photosensitive unit located at the edge of the dark area can receive the surrounding small-angle reflected light (the light represented by the solid line), and the photosensitive unit located at the center of the dark area can receive the transmitted light of the real finger or fake fingerprint material (the light represented by the dotted line).

[0099] Due to the existence of fake fingerprint materials, Figure 4 The object (real finger + fake fingerprint material) located above the display is thicker than Figure 3 The thickness of the object (real finger) located above the display screen, and the greater the thickness, the more transmitted light the photosensitive unit at the center of the dark area receives. Therefore, the transmitted light generated when a 2.5D fake fingerprint presses the screen is more than the transmitted light generated when a real finger presses the screen. Therefore, based on this difference, the present application can realize the authenticity identification of fingerprints.

[0100] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0101] It can be understood that the processing steps of the fingerprint recognition method in the present application can be implemented by a processor inside an electronic device. The processor can identify the authenticity of the fingerprint based on the fingerprint recognition method in the present application, and then perform further processing based on the fingerprint authenticity recognition result. For example, when the processor determines that the fingerprint is a genuine fingerprint, it can execute the unlocking operation of the electronic device, etc.

[0102] Figure 5 A schematic diagram of a fingerprint recognition method provided in an embodiment of the present application is provided, and an example is given in which the execution subject of the method is a processor inside an electronic device. Figure 5 As shown, the method mainly includes the following steps:

[0103] S1. When a pressing operation of a finger in a pressing area is detected, first fingerprint data and second fingerprint data corresponding to the fingerprint to be detected are respectively obtained, the first fingerprint data being data collected when a light-emitting unit corresponding to the pressing area is partially illuminated, and the second fingerprint data being data collected when a light-emitting unit corresponding to the pressing area is fully illuminated.

[0104] The first fingerprint data and the second fingerprint data acquired by the processor can be obtained by the optical detection component in the fingerprint recognition device according to the multi-path optical signal guided by the multi-path optical structure, and the first fingerprint data and the second fingerprint data are two sets of fingerprint data collected during a single pressing process of the object.

[0105] Specifically, when an object is detected pressing the display screen, the light-emitting unit corresponding to the fingerprint collection area (i.e., the pressing area) can be partially illuminated and fully illuminated in sequence under the action of the control signal, thereby obtaining the first fingerprint data and the second fingerprint data in sequence. Optionally, the light-emitting unit can also be fully illuminated first and then partially illuminated, thereby obtaining the second fingerprint data and the first fingerprint data in sequence.

[0106] Among them, the first fingerprint data collected when the light-emitting unit corresponding to the fingerprint collection area partially emits light, because the light spot formed by the partial light emission is an irregular light spot, the first fingerprint data can also be called irregular light spot data.

[0107] In addition, the second fingerprint data collected when all the light-emitting units corresponding to the fingerprint collection area emit light can also be called normal light spot data because the light spot formed by all the light emission is a complete light spot.

[0108] S2. Determine fingerprint feature information according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data, wherein the bright area data is data in the area where the light-emitting unit in the light-emitting state is located, and the dark area data is data in the area where the light-emitting unit in the extinguished state is located;

[0109] In this step, the processor determines the fingerprint feature information based on the bright area data and dark area data corresponding to the first fingerprint data and the second fingerprint data. Therefore, it can be considered that the fingerprint feature information is related to the bright area data and dark area data corresponding to the first fingerprint data and the second fingerprint data. Since the difference between a real finger and a 2.5D fake fingerprint is mainly reflected in the amount of transmitted light generated, and the difference can be reflected by the bright area data and dark area data corresponding to the first fingerprint data, the fingerprint feature information obtained by the processor can be used to determine the authenticity of the fingerprint to be detected.

[0110] S3. Determine the authenticity of the fingerprint to be detected based on the fingerprint feature information.

[0111] Since the difference between a real finger and a 2.5D fake fingerprint is mainly reflected in the amount of transmitted light generated, and this difference can be reflected by the bright area data and the dark area data corresponding to the first fingerprint data, after the processor obtains the bright area data and the dark area data corresponding to the first fingerprint data and the fingerprint feature information related to the second fingerprint data, the authenticity of the fingerprint to be detected can be determined according to the fingerprint feature information.

[0112] The present embodiment provides a fingerprint recognition method, which obtains first fingerprint data corresponding to the irregular light spot and second fingerprint data corresponding to the normal light spot during a single press, and then obtains bright area data and dark area data corresponding to the first fingerprint data and fingerprint feature information related to the second fingerprint data. Since the bright area data and dark area data corresponding to the first fingerprint data can be used to characterize the difference between a real finger and a 2.5D fake fingerprint, that is, the difference in the amount of transmitted light generated by the real finger and the 2.5D fake fingerprint, the authenticity of the fingerprint to be detected can be effectively determined based on the fingerprint feature information, thereby improving the accuracy of the fingerprint recognition result for the 2.5D fake fingerprint.

[0113] In some embodiments, determining fingerprint feature information according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data includes:

[0114] S21, determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data, and the second fingerprint data;

[0115] S22. Determine fingerprint feature information according to the calibration data.

[0116] Specifically, when performing under-screen fingerprint recognition, the light-emitting unit of the display screen will emit a light signal to the object placed on the display screen. In addition, a part of the light signal emitted by the light-emitting unit of the display screen (also called screen light leakage) is directly received by the optical detection component after multiple refractions, reflections, and diffractions. The optical detection component generates fingerprint raw data based on the received light signal. However, the fingerprint raw data includes not only fingerprint information, but also interference information (also called background noise) such as background noise of the display screen (such as screen light leakage), and the background noise of each optical detection component is different. Therefore, by calibrating the fingerprint raw data, such as regularizing it, the influence of the background noise difference of the optical detection component on the fingerprint raw data can be eliminated to ensure the accuracy of the fingerprint data.

[0117] In some embodiments, the calibration data includes first calibration data corresponding to the first fingerprint data and second calibration data corresponding to the second fingerprint data;

[0118] In this embodiment, the processor can perform regularization processing on the first fingerprint data and the second fingerprint data respectively, thereby eliminating the influence of the background noise difference of the optical detection component on the first fingerprint data and the second fingerprint data, so as to ensure the accuracy of the first fingerprint data and the second fingerprint data.

[0119] Correspondingly, determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data includes:

[0120] S211, performing regularization processing on the bright area data and the dark area data corresponding to the first fingerprint data by using a first type of preset data set to obtain first calibration data, wherein the first type of preset data set is a data set obtained when the light-emitting unit partially emits light and used to calibrate the fingerprint data collected when the light-emitting unit partially emits light during the pressing process;

[0121] Specifically, the first type of preset data set includes a first preset data set and a second preset data set, the first preset data set and the second preset data set are data sets acquired when the light-emitting unit partially emits light, and are used to calibrate the fingerprint data acquired when the light-emitting unit partially emits light during the pressing process;

[0122] Figure 6 A schematic diagram of obtaining a first preset data set provided in an embodiment of the present application, such as Figure 6 As shown, the first preset data set may be a data set obtained by simulating a user's finger using a flesh-colored flat-head fingerprint model 51 during the calibration phase of the optical detection component. The flesh-colored flat-head fingerprint model 51 is used to simulate a user's finger without fingerprints, that is, the flesh-colored flat-head fingerprint model 51 is equivalent to a finger with all fingerprint valleys. When obtaining the first preset data set, the flesh-colored flat-head fingerprint model 51 may be pressed on a fingerprint collection area (that is, a local area of ​​the display screen 12 corresponding to the optical detection component), and when the light-emitting unit corresponding to the fingerprint collection area partially emits light, the optical detection component determines the first preset data set based on the received light signal. Since the flesh-colored flat-head fingerprint model 51 is equivalent to a finger with all fingerprint valleys, the first preset data set not only contains information related to the light reflected by the central concave surface of the flesh-colored flat-head fingerprint model 51, but also includes interference information such as background noise of the display screen 12 (for example, light leakage information of the display screen 12).

[0123] Figure 7 A schematic diagram of obtaining a second preset data set provided in an embodiment of the present application, such as Figure 7As shown, the second preset data set can be a data set obtained by simulating the user's finger using the black flat-head fingerprint model 52 during the calibration stage of the optical detection component. The black flat-head fingerprint model 52 is used to simulate a pressing state without finger touch. When acquiring the second fingerprint data set, the black flat-head fingerprint model 52 can be pressed on the fingerprint collection area. When the light-emitting units corresponding to the fingerprint collection area are in the same partial light emission, the optical detection component determines the second preset data set based on the received light signal. Since the black flat-head fingerprint model 52 absorbs the light transmitted above the display screen, the second fingerprint data only includes interference information such as the background noise of the display screen (such as light leakage information of the display screen 12), that is, it only includes the background noise of the optical detection component.

[0124] Fingerprint data without background noise can be obtained by subtracting the second preset data set from the first preset data set. In this embodiment, the first preset data set and the second preset data set are used to perform regularization processing on the first fingerprint data corresponding to the fingerprint to be detected, thereby eliminating the influence of the background noise of different optical detection components on the first fingerprint data and improving the accuracy of fingerprint recognition.

[0125] For ease of description, the first preset data set and the second preset data set are represented as H_Fleshspot and H_blackspot respectively, and the first fingerprint data of the fingerprint to be detected is represented as Spotdata. The first fingerprint data can be regularized by the following formula:

[0126] Spotdata=(Spotdata-H_blackspot) / (H_Fleshspot-H_blackspot)

[0127] The first calibration data corresponding to the first fingerprint data can be calculated by the above formula. It should be noted that the calculation method here is only to illustrate the specific principle of regularization processing, and matrix operations can also be used in actual calculations to improve processing speed.

[0128] It can be understood that the above-mentioned implementation method of regularization processing is only an example. This embodiment does not limit the specific implementation method of regularization processing. The first fingerprint data can also be regularized by using the first preset data set and the second preset data set through other suitable regularization processing methods.

[0129] S212. Regularize the second fingerprint data using a second type of preset data set to obtain second calibration data, wherein the first type of preset data set is a data set obtained when all the light-emitting units are illuminated and used to calibrate the fingerprint data collected when all the light-emitting units are illuminated during the pressing process.

[0130] Specifically, the second category of preset data sets includes a third preset data set and a fourth preset data set. The third preset data set and the fourth preset data set are data sets obtained when all the light-emitting units are illuminated and are used to calibrate the fingerprint data collected when all the light-emitting units are illuminated during the pressing process.

[0131] In this embodiment, the process of obtaining the third preset data set can refer to the process of obtaining the first preset data set in the above text for the basic principle. The difference is that when obtaining the third preset data set, the flesh-colored flat-head fingerprint model 51 can be pressed on the fingerprint collection area (i.e., the local area of ​​the display screen 12 corresponding to the optical detection component), and when all the light-emitting units corresponding to the fingerprint collection area are illuminated, the optical detection component determines the third preset data set based on the received light signal. The third preset data set not only contains information related to the light reflected by the central concave surface of the flesh-colored flat-head fingerprint model 51, but also includes interference information such as background noise of the display screen 12 (such as light leakage information of the display screen 12).

[0132] In addition, the process of obtaining the fourth preset data set can refer to the process of obtaining the second preset data set in the previous text for the basic principle. The difference is that when obtaining the fourth fingerprint data set, the black flat-head fingerprint model 52 can be pressed on the fingerprint collection area. When the light-emitting unit corresponding to the fingerprint collection area is fully illuminated, the optical detection component determines the fourth preset data set based on the received light signal. The fourth fingerprint data only includes interference information such as the background noise of the display screen (such as the light leakage information of the display screen 12), that is, only includes the background noise of the optical detection component.

[0133] Fingerprint data without background noise can be obtained by subtracting the fourth preset data set from the third preset data set. In this embodiment, the third preset data set and the fourth preset data set are used to perform regularization processing on the second fingerprint data corresponding to the fingerprint to be detected, thereby eliminating the influence of the background noise of different optical detection components on the second fingerprint data and improving the accuracy of fingerprint recognition.

[0134] For ease of description, the third preset data set and the fourth preset data set are represented as H_Flesh and H_black respectively, and the second fingerprint data of the fingerprint to be detected is represented as Nordata. Then, the second fingerprint data can be regularized by the following formula:

[0135] Nordata=(Nordata-H_black) / (H_Flesh-H_black)

[0136] The second calibration data corresponding to the second fingerprint data can be calculated by the above formula. It should be noted that the calculation method here is only to illustrate the specific principle of regularization processing, and matrix operations can also be used in actual calculations to improve processing speed.

[0137] It can be understood that the above-mentioned implementation method of regularization processing is only an example. This embodiment does not limit the specific implementation method of regularization processing. The third preset data set and the fourth preset data set can also be used to regularize the second fingerprint data through other suitable regularization processing methods.

[0138] Therefore, by regularizing the first fingerprint data and the second fingerprint data respectively, the first calibration data and the second calibration data are obtained. Based on the performance difference between the real finger and the 2.5D fake fingerprint, if it is a 2.5D fake fingerprint, the dark area data in the first calibration data will usually be larger than the fingerprint data at the corresponding position in the second calibration data; if it is a real finger, the dark area data in the first calibration data will usually be smaller than the fingerprint data at the corresponding position in the second calibration data. Therefore, based on the above principle, the authenticity identification of the fingerprint to be detected can be achieved.

[0139] In some embodiments, when the difference in the amount of transmitted light generated by the fake fingerprint material and the real finger is large, for example, the fake fingerprint material is relatively thick, the authenticity of the fingerprint can be directly identified based on the data features of the fingerprint data itself.

[0140] In this case, the fingerprint feature information includes the repetition of fingerprint data and the size relationship between fingerprint data, wherein the repetition is used to characterize the similarity between the first calibration data and the second calibration data, and the size relationship is the data size correlation relationship between the first calibration data and the second calibration data.

[0141] Correspondingly, the fingerprint feature information is determined based on the calibration data, including:

[0142] S221a, selecting first sub-data from the first calibration data, the first sub-data being row data including bright area data and dark area data, and / or the first sub-data being column data including bright area data and dark area data;

[0143] S221b, selecting second sub-data from the second calibration data, where the position of the second sub-data in the second fingerprint data is the same as the position of the first sub-data in the first fingerprint data;

[0144] S221c, determining the repetition degree between the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data;

[0145] S221d, determining that the dark area data in the first sub-data is smaller than the data amount of the fingerprint data at the corresponding position in the second sub-data, the data amount being used to characterize the size relationship between the first sub-data and the second sub-data.

[0146] Specifically, Figure 8 This is a schematic diagram of selecting the first sub-data from the first calibration data according to an embodiment of the present application. Figure 8 As shown in the figure, the first calibration data includes bright area data (white circle part in the figure) and dark area data (black circle part in the figure). When selecting the first sub-data, because it is necessary to rely on the dark area data for authenticity identification, the selected first sub-data is the row data and / or column data that includes both the bright area data and the dark area data. For example, you can select Figure 8 The m-th row data and / or the n-th column data in are taken as the first sub-data.

[0147] Fig. 9 This is a schematic diagram of selecting the second sub-data from the second calibration data according to an embodiment of the present application. Fig. 9 As shown, the second calibration data only includes the bright area data (the white circle part in the figure). When the second sub-data is selected, the position of the second sub-data in the second fingerprint data corresponds to the same position of the first sub-data in the first fingerprint data. Figure 8 The mth row of the first calibration data in is taken as the first sub-data, and the same selection is made Fig. 9 The mth row data of the second calibration data in is used as the second sub-data.

[0148] After selecting the first sub-data and the second sub-data, the processor determines the fingerprint feature information based on the first sub-data and the second sub-data. In this embodiment, the fingerprint feature information specifically includes the bright area data in the first sub-data, the repetition degree of the fingerprint data at the corresponding position in the second sub-data, and the amount of dark area data in the first sub-data that is smaller than the fingerprint data at the corresponding position in the second sub-data. Therefore, the authenticity of the fingerprint to be detected can be determined based on the above fingerprint feature information.

[0149] In some embodiments, determining the authenticity of a fingerprint to be detected based on fingerprint feature information includes:

[0150] S311, determining whether the first sub-data and the second sub-data are both valid according to the repetition degree of the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data;

[0151] S312: When it is determined that both the first sub-data and the second sub-data are valid, if the amount of dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data and reaches a first preset threshold, the fingerprint to be detected is determined to be a true fingerprint.

[0152] Among them, since the first fingerprint data and the second fingerprint data are two sets of fingerprint data collected during a single pressing process, during which the pressed area of ​​the object does not change significantly, the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data are highly similar. Therefore, the repetition degree of the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data can be used to determine the validity of the first fingerprint data and the second fingerprint data, that is, the validity of the first sub-data and the second sub-data.

[0153] When it is determined that both the first sub-data and the second sub-data are valid data, if the dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data and reaches a first preset threshold, the fingerprint to be detected is determined to be a true fingerprint, otherwise, the fingerprint to be detected is determined to be a false fingerprint.

[0154] In some embodiments, determining whether both the first sub-data and the second sub-data are valid is performed based on the repetition degree between the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data, including: if the amount of data of the bright area data in the first sub-data equal to the fingerprint data at the corresponding position in the second sub-data reaches a second preset threshold, determining that both the first sub-data and the second sub-data are valid.

[0155] Specifically, if the data amount of the bright area data in the first sub-data is equal to the fingerprint data at the corresponding position in the second sub-data and reaches the second preset threshold, it means that the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data are highly similar. Therefore, it can be determined that the first fingerprint data and the second fingerprint data are both valid data, and thus the first sub-data and the second sub-data are also valid data. Therefore, the fingerprint authenticity identification result obtained based on the first sub-data and the second sub-data has high accuracy and credibility.

[0156] It can be understood that in this embodiment, the bright area data in the first sub-data is equal to the fingerprint data at the corresponding position in the second sub-data, which may be that the two data are completely equal, or the difference between the two data is less than a certain threshold. Therefore, in both of the above two cases, it can be considered that the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data are highly similar.

[0157] In addition, if the data amount of the bright area data in the first sub-data is equal to the fingerprint data at the corresponding position in the second sub-data does not reach the second preset threshold, it means that the similarity between the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data is low, and it is determined that both the first sub-data and the second sub-data are invalid data. At this time, it is necessary to re-collect the first fingerprint data and the second fingerprint data, and the electronic device can output a prompt message to press again to re-acquire the first fingerprint data and the second fingerprint data.

[0158] Fig.10 This is an example diagram of fingerprint authenticity identification in the embodiment of the present application. Fig.10 As shown, the horizontal axis of the coordinate axis represents the position of the data, the vertical axis represents the data size, the first curve represents the first sub-data of a row containing both bright area data and dark area data in the first calibration data, and the second curve represents the second sub-data at the corresponding position in the second calibration data. Fig.10 In the curves shown, the repetition of the bright area data at both ends of the horizontal axis is higher in the two curves, so both curves are valid data. In addition, for the dark area data located in the middle of the horizontal axis, since the first curve has a larger portion below the second curve, it means that the dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data, and the data volume reaches the first preset threshold, so it can be determined that the fingerprint to be detected is a true fingerprint.

[0159] Fig.11 Another example diagram of fingerprint authenticity identification in the embodiment of the present application is shown in FIG. Fig.11 As shown, the horizontal axis of the coordinate axis represents the position of the data, the vertical axis represents the size of the data, the first curve represents the first sub-data of a column containing both bright area data and dark area data in the first calibration data, and the second curve represents the second sub-data at the corresponding position in the second calibration data. Fig.11 In the curves shown, the repetition of the bright area data at both ends of the horizontal axis is higher in the two curves, so both curves are valid data. In addition, for the dark area data located in the middle of the horizontal axis, since the first curve has a larger portion below the second curve, it means that the dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data, and the data volume reaches the first preset threshold, so it can be determined that the fingerprint to be detected is a true fingerprint.

[0160] Fig.12 Another example diagram of fingerprint authenticity identification in the embodiment of the present application is shown in FIG. Fig.12 As shown, the horizontal axis of the coordinate axis represents the position of the data, the vertical axis represents the data size, the first curve represents the first sub-data of a row containing both bright area data and dark area data in the first calibration data, and the second curve represents the second sub-data at the corresponding position in the second calibration data. Fig.12 In the curves shown, the repetition of the bright area data at both ends of the horizontal axis is higher in the two curves, so both curves are valid data. In addition, for the dark area data located in the middle of the horizontal axis, since the first curve has a larger portion above the second curve, it means that the dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data and the data volume does not reach the first preset threshold, so it can be determined that the fingerprint to be detected is a false fingerprint.

[0161] Fig.13Another example diagram of fingerprint authenticity identification in the embodiment of the present application is shown in FIG. Fig.13 As shown, the horizontal axis of the coordinate axis represents the position of the data, the vertical axis represents the size of the data, the first curve represents the first sub-data of a column containing both bright area data and dark area data in the first calibration data, and the second curve represents the second sub-data at the corresponding position in the second calibration data. Fig.13 In the curves shown, the repetition of the bright area data at both ends of the horizontal axis is higher in the two curves, so both curves are valid data. In addition, for the dark area data located in the middle of the horizontal axis, since the first curve has a larger portion above the second curve, it means that the dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data and the data volume does not reach the first preset threshold, so it can be determined that the fingerprint to be detected is a false fingerprint.

[0162] Thus, based on Figures 10 to 13 As shown in the example diagram, the authenticity of fingerprints can be effectively identified through the technical solution of the present application, thereby improving the security performance of electronic equipment.

[0163] In some embodiments, when the difference in the amount of transmitted light generated by the fake fingerprint material and the real finger is small, for example, the fake fingerprint material is relatively thin and transparent (such as gelatin), the authenticity of the fingerprint cannot be accurately identified directly based on the data features of the fingerprint data itself. At this time, the authenticity of the fingerprint can be identified in combination with more in-depth data features obtained from the fingerprint data.

[0164] In this case, the fingerprint feature information includes the size relationship of the fingerprint data, the mean value of the fingerprint data, the difference of the mean value of the fingerprint data, and the difference between the dark area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data.

[0165] Correspondingly, the fingerprint feature information is determined based on the calibration data, including:

[0166] S222a, selecting first sub-data from the first calibration data, the first sub-data being row data including bright area data and dark area data, and / or the first sub-data being column data including bright area data and dark area data;

[0167] S222b, selecting second sub-data from the second calibration data, where a position of the second sub-data in the second fingerprint data is the same as a position of the first sub-data in the first fingerprint data;

[0168] S222c, determining that the dark area data in the first sub-data is smaller than the data volume of the fingerprint data at the corresponding position in the second sub-data;

[0169] S222d, determining a first data mean of dark area data in the first sub-data, a second data mean of data at a corresponding position in the second sub-data, and a difference between the first data mean and the second data mean;

[0170] S222e, determining a difference between each dark area data in the first sub-data and the fingerprint data at a corresponding position in the second sub-data, and determining a sum of absolute values ​​of each difference, wherein the sum is used to characterize a difference between the first sub-data and the second sub-data.

[0171] Specifically, assuming that both the first sub-data and the second sub-data are row data, the first sub-data and the second sub-data are represented as RowSpotData and RowNorData respectively, then the first data mean AvgRowSpotData of the dark area data in RowSpotData (for example, from the mth data to the nth data), the second data mean AvgRowNorData of the data at the corresponding position in RowNorData (that is, also from the mth data to the nth data), and the difference AvgRowSub between AvgRowNorData and AvgRowSpotData are calculated respectively. Similarly, if both the first sub-data and the second sub-data are column data, the same processing is performed.

[0172] In addition, for each dark area data in RowSpotData and the fingerprint data at the corresponding position in RowNorData, the difference is calculated pixel by pixel, and the sum sumRow of the absolute value of each difference is determined. The sum sumRow can be used to represent the difference between the dark area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data. Similarly, if both the first sub-data and the second sub-data are column data, the same process is performed.

[0173] In some embodiments, determining the authenticity of a fingerprint to be detected based on fingerprint feature information includes:

[0174] S313, inputting at least one of the amount of dark area data in the first sub-data that is smaller than the fingerprint data at the corresponding position in the second sub-data, the first data mean, the second data mean, the difference, and the sum into a pre-trained decision tree model to obtain a score indicating that the fingerprint to be detected is a true fingerprint;

[0175] S314: Determine the authenticity of the fingerprint to be detected based on the comparison result between the score and the preset fingerprint threshold.

[0176] Among them, the decision tree model is trained based on the fingerprint feature information of each fingerprint sample in the fingerprint sample set and the authenticity result of each fingerprint sample. The fingerprint feature information of each fingerprint sample has the same feature type as the fingerprint feature information of the fingerprint to be detected. For example, the feature type of the fingerprint feature information includes the first data mean of the first sub-data, the second data mean of the second sub-data, the difference between the first data mean and the second data mean, the sum of the absolute values ​​of each difference (for each dark area data in RowSpotData and the fingerprint data at the corresponding position in RowNorData, the difference is calculated pixel by pixel), or any combination thereof. It should be understood that the feature type of the fingerprint feature information here is illustrated by example, and the present embodiment is not limited to this.

[0177] The fingerprint samples in the fingerprint sample set may include, for example, real fingers and fake fingers in various scenarios, for example, real fingers and fake fingers in low temperature scenarios, high temperature scenarios, normal temperature scenarios, oily conditions, dry fingers and / or wet fingers. Accordingly, the fingerprint feature information of the fingerprint sample is the fingerprint feature information determined according to the fingerprint data corresponding to the fingerprint sample in various scenarios.

[0178] When training a decision tree model, each feature type of fingerprint feature information can be regarded as a decision node, and each decision node is used to classify the fingerprint sample. The decision tree model generated by the training can include a judgment threshold and weight corresponding to each feature type.

[0179] When performing fingerprint recognition, the fingerprint feature information generated based on the fingerprint data corresponding to the fingerprint to be detected is input into a pre-trained decision tree model, and the pre-trained decision tree model generates a score for the fingerprint to be detected based on a predetermined judgment threshold and weight.

[0180] For example, if the feature type of the fingerprint feature information includes the first data mean of the first sub-data, the second data mean of the second sub-data, the difference between the first data mean and the second data mean, and the sum of the absolute values ​​of each difference, when training the decision tree model, the first data mean of the first sub-data, the second data mean of the second sub-data, the difference between the first data mean and the second data mean, and the sum of the absolute values ​​of each difference corresponding to each fingerprint sample in the fingerprint sample set can be extracted as the fingerprint feature information corresponding to the fingerprint sample, and the extracted fingerprint feature information of each fingerprint sample and the true and false structure corresponding to each fingerprint sample are input into the decision tree model for training to obtain a trained prediction model, which includes a first judgment threshold and a first weight corresponding to the first data mean of the first sub-data, a second judgment threshold and a second weight corresponding to the second data mean of the second sub-data, a third judgment threshold and a third weight corresponding to the difference between the first data mean and the second data mean, and a fourth judgment threshold and a fourth weight corresponding to the sum of the absolute values ​​of each difference. When identifying the fingerprint to be detected, the first data mean of the first sub-data of the fingerprint to be detected, the second data mean of the second sub-data, the difference between the first data mean and the second data mean, and the sum of the absolute values ​​of each difference can be extracted, and the trained decision tree model determines the score indicating that the fingerprint to be detected is a true fingerprint according to the comparison result of the first data mean of the first sub-data of the fingerprint to be detected and the first judgment threshold, the comparison result of the first data mean of the first sub-data of the fingerprint to be detected and the second judgment threshold, the comparison result of the difference between the first data mean and the second data mean of the fingerprint to be detected and the third judgment threshold, the comparison result of the sum of the absolute values ​​of each difference of the fingerprint to be detected and the fourth judgment threshold, and the weights corresponding to the various judgment conditions.

[0181] In addition, the preset fingerprint threshold can be flexibly set according to the user's security level requirements. For example, in application scenarios with low security level requirements, such as unlocking electronic devices through fingerprint verification, the preset fingerprint threshold can be set relatively low, such as 0.5. In application scenarios with high security level requirements, such as paying fees through fingerprint verification, the preset fingerprint threshold can be set relatively high, such as 0.7.

[0182] In this embodiment, based on the above fingerprint feature information, a pre-trained decision tree model is used for scoring, and the authenticity of the fingerprint to be detected is determined according to the comparison result between the score and the preset fingerprint threshold, so that the true and false fingerprints can be accurately identified.

[0183] In some embodiments, the calibration data includes third calibration data corresponding to the first fingerprint data;

[0184] Determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data includes:

[0185] S213, selecting third sub-data from the bright area data corresponding to the first fingerprint data, and determining a third data mean of the third sub-data;

[0186] S214, selecting fourth sub-data from the second fingerprint data, determining a fourth data mean of the fourth sub-data, wherein a position of the fourth sub-data in the second fingerprint data corresponds to a position of the third sub-data in the first fingerprint data;

[0187] S215, determining a ratio of the third data mean to the fourth data mean;

[0188] S216: Based on the bright area data and dark area data of the first fingerprint data, the second fingerprint data, and the ratio, regularize the first fingerprint data to obtain third calibration data.

[0189] Fig.14 : is a schematic diagram of the first fingerprint data in the embodiment of the present application, wherein the data in the solid circle is the dark area data corresponding to the first fingerprint data, and the data outside the solid circle and in the box is the bright area data corresponding to the first fingerprint data; when selecting the third sub-data, one or more sub-data in the bright area data can be selected, for example, Fig.14 P11, P21, P31, and P41 in are used as the third sub-data.

[0190] Fig.15 : is a schematic diagram of the second fingerprint data in the embodiment of the present application, wherein the data in the dotted circle is the fingerprint data of the corresponding position of the dark area data in the first fingerprint data in the second fingerprint data, and the data outside the dotted circle and in the box is the fingerprint data of the corresponding position of the bright area data in the first fingerprint data in the second fingerprint data. Fig.14 When P11, P21, P31, and P41 in are used as the third sub-data, Fig.15 P12, P22, P32, and P42 in are used as the fourth sub-data.

[0191] After selecting the third sub-data and the fourth sub-data, the corresponding data means are calculated respectively. For example, the data means corresponding to P11, P21, P31, and P41 are represented by AvgSpotPart1, AvgSpotPart2, AvgSpotPart3, and AvgSpotPart4 respectively, and the data means corresponding to P12, P22, P32, and P42 are represented by AvgNorPart1, AvgNorPart2, AvgNorPart3, and AvgNorPart4 respectively. Then, the ratio of the third data mean to the fourth data mean includes K1, K2, K3, and K4, where:

[0192] K1=AvgNorPart1 / AvgSpotPart1

[0193] K2=AvgNorPart2 / AvgSpotPart2

[0194] K3=AvgNorPart3 / AvgSpotPart3

[0195] K4=AvgNorPart4 / AvgSpotPart4

[0196] Then, calculate the mean K of the ratios:

[0197] K=(K1+K2+K3+K4) / 4

[0198] Finally, based on the first fingerprint data SpotData and the second fingerprint data NorData, the first fingerprint data is regularized by the following formula to obtain the third calibration data SpotNomalized:

[0199] SpotNomalized=SpotData / (NorData*K)

[0200] Therefore, by performing regularization processing on the first fingerprint data, the accuracy of the fingerprint data can be improved, thereby improving the accuracy of the fingerprint recognition result.

[0201] In some embodiments, determining the authenticity of a fingerprint to be detected based on fingerprint feature information includes:

[0202] S315, determining quantitative feature information of the bright area data and / or the dark area data corresponding to the third calibration data, where the quantitative feature information includes at least one of a data mean, a data variance, a data standard deviation, and a gradient value;

[0203] S316, inputting the quantitative feature information into a pre-trained decision tree model to obtain a score indicating that the fingerprint to be detected is a true fingerprint;

[0204] S317: Determine the authenticity of the fingerprint to be detected based on the comparison result between the score and the preset fingerprint threshold.

[0205] Specifically, when determining the quantitative feature information of the bright area data and / or the dark area data corresponding to the third calibration data, the fifth sub-data and the sixth sub-data may be selected from the dark area data of the third calibration data, and a fifth data mean, data variance, and data standard deviation of the fifth sub-data, as well as a sixth data mean, data variance, and data standard deviation of the sixth sub-data may be determined, and the fifth sub-data and the sixth sub-data are at different positions;

[0206] For example, refer to Fig.14 After obtaining the third calibration data, you can select Fig.14 The data with the same position as C1 in the third calibration data is used as the fifth sub-data, that is, the center position data of the dark area data in the third calibration data. Fig.14 The data with the same positions of L1, R1, U1 and D1 are used as the sixth sub-data, namely, the edge position data of the dark area data in the third calibration data.

[0207] After selecting the fifth sub-data and the sixth sub-data, the corresponding data mean, data variance, and data standard deviation are calculated respectively. For example, the fifth data mean avgcenter, variance varCenter, etc. corresponding to the fifth sub-data, and the sixth data mean avgL / avgR / avgU / avgD, variance varL / varR / varU / varD, etc. corresponding to the sixth sub-data.

[0208] In addition, when determining the quantitative feature information of the bright area data and / or the dark area data corresponding to the third calibration data, the seventh sub-data may be selected from the bright area data of the third calibration data to determine the seventh data mean, data variance, and data standard deviation of the seventh sub-data;

[0209] For example, refer to Fig.14 After obtaining the third calibration data, you can select Fig.14 The data with the same position among P11, P21, P31, and P41 are taken as the seventh sub-data.

[0210] After selecting the seventh sub-data, the corresponding data mean, data variance, and data standard deviation are calculated respectively. For example, the seventh data mean avgPart1 / avgPart2 / avgPart3 / avgPart4 corresponding to the seventh sub-data, etc.

[0211] In addition, after obtaining the fifth data mean, the sixth data mean and the seventh data mean, the gradient values ​​between different areas in the third calibration data, that is, the two gradients in four directions from the peripheral area to the central area, can be determined based on the fifth data mean, the sixth data mean and the seventh data mean.

[0212] For example, by subtracting the mean data of L1 from the mean data of P11, we can get the first gradient on the left: Gra1 = avgPart1 - avgL; by subtracting the mean data of C1 from the mean data of L1, we can get the second gradient on the left: GraL2 = avgL - avgCenter. Similarly, we can get two gradients in other directions.

[0213] It can be understood that in this embodiment, the specific shape, specific position and specific number of the sixth sub-data and the seventh sub-data are not limited. Fig.16 and Fig.17 These are two other schematic diagrams for selecting the sixth sub-data and the seventh sub-data in the embodiment of the present application. The specific positions of the sixth sub-data and the seventh sub-data can be adjusted, and similarly, the specific shapes and specific quantities can also be adjusted.

[0214] In this embodiment, based on the above fingerprint feature information, a pre-trained decision tree model is used for scoring, and the authenticity of the fingerprint to be detected is determined according to the comparison result between the score and the preset fingerprint threshold, so that the true and false fingerprints can be accurately identified.

[0215] In some embodiments, the processor may also perform regularization processing on the first fingerprint data and the second fingerprint data respectively, and obtain the size relationship of the fingerprint data of the first calibration data and the second calibration data, the mean of the fingerprint data, the difference of the mean of the fingerprint data, the difference between the dark area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data, and perform regularization processing on the first fingerprint data and obtain quantitative feature information of the bright area data and / or dark area data corresponding to the third calibration data, and then input the above fingerprint feature information into a pre-trained decision tree model to obtain a score indicating that the fingerprint to be detected is a true fingerprint; and determine the authenticity of the fingerprint to be detected based on the comparison result of the score and the preset fingerprint threshold.

[0216] The specific process of the above processing steps can refer to the above description, which will not be repeated here. This embodiment can further improve the accuracy of fingerprint authenticity recognition results by combining multiple fingerprint feature information to perform scoring judgment.

[0217] It should be understood that, although the various steps in the flowcharts in the above-described embodiments are sequentially displayed according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and they can be executed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily to be carried out sequentially, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0218] In some embodiments, a fingerprint recognition device is provided. Fig.18 A schematic diagram of a fingerprint recognition device provided in an embodiment of the present application, such as Fig.18 As shown, the device comprises:

[0219] The fingerprint data acquisition module 100 is used to respectively acquire first fingerprint data and second fingerprint data corresponding to the fingerprint to be detected when a pressing operation of a finger in a pressing area is detected, wherein the first fingerprint data is data collected when a light-emitting unit corresponding to the pressing area partially emits light, and the second fingerprint data is data collected when the light-emitting unit corresponding to the pressing area fully emits light;

[0220] The feature information determination module 200 is used to determine the fingerprint feature information according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data, wherein the bright area data is the data in the area where the light-emitting unit in the light-emitting state is located, and the dark area data is the data in the area where the light-emitting unit in the extinguished state is located;

[0221] The fingerprint authenticity determination module 300 is used to determine the authenticity of the fingerprint to be detected according to the fingerprint feature information.

[0222] For the specific definition of the fingerprint recognition device, please refer to the definition of the fingerprint recognition method above, which will not be repeated here. Each module in the above fingerprint recognition device can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0223] The present application provides a fingerprint recognition device, which obtains first fingerprint data corresponding to an irregular light spot and second fingerprint data corresponding to a normal light spot during a single press, and then obtains bright area data and dark area data corresponding to the first fingerprint data and fingerprint feature information related to the second fingerprint data. Since the bright area data and dark area data corresponding to the first fingerprint data can be used to characterize the difference between a real finger and a 2.5D fake fingerprint, that is, the difference in the amount of transmitted light generated by the real finger and the 2.5D fake fingerprint, the authenticity of the fingerprint to be detected can be effectively determined based on the fingerprint feature information, thereby improving the accuracy of the fingerprint recognition result for the 2.5D fake fingerprint.

[0224] In some embodiments, an electronic device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the above-mentioned fingerprint recognition method is implemented when the processor executes the program.

[0225] In the above terminal device, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines, such as through a bus connection. The memory stores computer-executable instructions for implementing the data access control method, including at least one software function module that can be stored in the memory in the form of software or firmware. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory.

[0226] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc. The memory is used to store programs, and the processor executes the programs after receiving the execution instructions. Furthermore, the software programs and modules in the above-mentioned memory may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.

[0227] The processor may be an integrated circuit chip having the ability to process signals. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. The disclosed methods, steps and logic block diagrams in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0228] In some embodiments, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the steps of the various method embodiments of the present application.

[0229] In some embodiments, a computer program product is provided, including a computer program, which implements the steps of the method embodiments of the present application when executed by a processor.

[0230] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0231] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the application disclosed herein. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0232] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A fingerprint recognition method, characterized in that: include: When a pressing operation of a finger in the pressing area is detected, first fingerprint data and second fingerprint data corresponding to the fingerprint to be detected are respectively obtained, wherein the first fingerprint data is data collected when a light-emitting unit corresponding to the pressing area is partially illuminated, and the second fingerprint data is data collected when the light-emitting unit corresponding to the pressing area is fully illuminated; determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data; determining fingerprint feature information according to the calibration data, wherein the bright area data is data in an area where the light-emitting unit in the light-emitting state is located, and the dark area data is data in an area where the light-emitting unit in the light-off state is located, and the calibration data includes first calibration data corresponding to the first fingerprint data and second calibration data corresponding to the second fingerprint data; Determining the authenticity of the fingerprint to be detected according to the fingerprint feature information; When the fingerprint feature information includes the repetition degree of fingerprint data and the size relationship between fingerprint data, determining the authenticity of the fingerprint to be detected according to the fingerprint feature information includes: determining whether the first sub-data and the second sub-data are both valid according to the repetition degree between the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data, wherein the first sub-data is selected from the first calibration data, the second sub-data is selected from the second calibration data, and the position of the second sub-data in the second fingerprint data is the same as the position of the first sub-data in the first fingerprint data; When it is determined that both the first sub-data and the second sub-data are valid, if the dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data and reaches a first preset threshold, the fingerprint to be detected is determined to be a true fingerprint.

2. The method according to claim 1, characterized in that The step of determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data includes: Regularizing the bright area data and the dark area data corresponding to the first fingerprint data by using a first type of preset data set to obtain the first calibration data, wherein the first type of preset data set is a data set obtained when the light-emitting unit partially emits light and used to calibrate the fingerprint data collected when the light-emitting unit partially emits light during the pressing process; The second fingerprint data is regularized by a second type of preset data set to obtain the second calibration data, wherein the second type of preset data set is a data set obtained when the light-emitting units are fully illuminated and used to calibrate the fingerprint data collected when the light-emitting units are fully illuminated during the pressing process.

3. The method according to claim 1, characterized in that The first sub-data is row data including bright area data and dark area data, and / or the first sub-data is column data including bright area data and dark area data; the data amount is used to characterize the size relationship between the first sub-data and the second sub-data.

4. The method according to claim 1, characterized in that: Determining whether both the first sub-data and the second sub-data are valid according to the repetition degree of the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data includes: If the amount of bright area data in the first sub-data is equal to the fingerprint data at the corresponding position in the second sub-data and reaches a second preset threshold, it is determined that both the first sub-data and the second sub-data are valid.

5. The method according to claim 2, characterized in that: When the fingerprint feature information includes the size relationship of fingerprint data, the mean value of fingerprint data, the difference of the mean value of fingerprint data, and the difference between the dark area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data, the determining of the fingerprint feature information according to the calibration data includes: Selecting first sub-data from the first calibration data, the first sub-data being row data including the bright area data and the dark area data, and / or the first sub-data being column data including the bright area data and the dark area data; selecting second sub-data from the second calibration data, wherein a position of the second sub-data in the second fingerprint data is the same as a position of the first sub-data in the first fingerprint data; Determine that the dark area data in the first sub-data is smaller than the data amount of the fingerprint data at the corresponding position in the second sub-data; Determine a first data mean of dark area data in the first sub-data, a second data mean of data at corresponding positions in the second sub-data, and a difference between the first data mean and the second data mean; Determine the difference between each dark area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data, and determine the sum of the absolute values ​​of each difference, wherein the sum is used to characterize the difference between the first sub-data and the second sub-data.

6. The method according to claim 1, characterized in that The calibration data also includes third calibration data corresponding to the first fingerprint data; The step of determining calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data and the second fingerprint data includes: Selecting third sub-data from the bright area data corresponding to the first fingerprint data, and determining a third data mean value of the third sub-data; Selecting fourth sub-data from the second fingerprint data, determining a fourth data mean of the fourth sub-data, wherein a position of the fourth sub-data in the second fingerprint data corresponds to the same position of the third sub-data in the first fingerprint data; Determining a ratio of the third data mean to the fourth data mean; Based on the bright area data and the dark area data of the first fingerprint data, the second fingerprint data and the ratio, regularization processing is performed on the first fingerprint data to obtain the third calibration data.

7. The method according to claim 6, characterized in that When the fingerprint feature information includes quantitative feature information, determining the fingerprint feature information according to the calibration data includes: Determine quantitative feature information of the bright area data and / or the dark area data corresponding to the third calibration data, wherein the quantitative feature information includes at least one of a data mean, a data variance, a data standard deviation, and a gradient value.

8. The method according to claim 5 or 7, characterized in that: Determining the authenticity of the fingerprint to be detected according to the fingerprint feature information includes: Inputting the fingerprint feature information into a pre-trained decision tree model to obtain a score indicating that the fingerprint to be detected is a true fingerprint; The authenticity of the fingerprint to be detected is determined based on a comparison result between the score and a preset fingerprint threshold.

9. A fingerprint recognition device, characterized in that: include: A fingerprint data acquisition module, used to respectively acquire first fingerprint data and second fingerprint data corresponding to the fingerprint to be detected when a pressing operation of a finger in a pressing area is detected, wherein the first fingerprint data is data collected when a light-emitting unit corresponding to the pressing area is partially illuminated, and the second fingerprint data is data collected when the light-emitting unit corresponding to the pressing area is fully illuminated; a feature information determination module, configured to determine calibration data according to the bright area data and the dark area data corresponding to the first fingerprint data, and the second fingerprint data; and determine fingerprint feature information according to the calibration data, wherein the bright area data is data in an area where the light-emitting unit in the light-emitting state is located, and the dark area data is data in an area where the light-emitting unit in the light-off state is located, and the calibration data includes first calibration data corresponding to the first fingerprint data and second calibration data corresponding to the second fingerprint data; A fingerprint authenticity determination module, used to determine the authenticity of the fingerprint to be detected according to the fingerprint feature information; When the fingerprint feature information includes the repetition of fingerprint data and the size relationship between fingerprint data, the fingerprint authenticity determination module is specifically used to determine whether the first sub-data and the second sub-data are both valid according to the repetition of the bright area data in the first sub-data and the fingerprint data at the corresponding position in the second sub-data, the first sub-data is selected from the first calibration data, the second sub-data is selected from the second calibration data, and the position of the second sub-data in the second fingerprint data is the same as the position of the first sub-data in the first fingerprint data; When it is determined that both the first sub-data and the second sub-data are valid, if the dark area data in the first sub-data is smaller than the fingerprint data at the corresponding position in the second sub-data and reaches a first preset threshold, the fingerprint to be detected is determined to be a true fingerprint.

10. An electronic device, characterized in that: include: A memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the fingerprint recognition method according to any one of claims 1 to 8 when executing the program.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the fingerprint recognition method according to any one of claims 1 to 8.

12. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.

Images