Fingerprint anti-counterfeiting method, device and equipment

Abstract

The embodiment of the present application provides a kind of fingerprint anti-counterfeiting method, device and equipment, in the fingerprint anti-counterfeiting method described above, fingerprint anti-counterfeiting equipment is collected at least two frames of fingerprint image after fingerprint sensor, at least two frames of fingerprint image are matched and identified, if the result of matching identification is that the fingerprint in at least two frames of fingerprint image is matched with the fingerprint stored in advance, at least two frames of fingerprint image are identified for anti-counterfeiting, if the result of anti-counterfeiting identification is that the fingerprint in at least two frames of fingerprint image is the fingerprint of real hand, then determine that the fingerprint in at least two frames of fingerprint image passes identity authentication, so that the fingerprint of real hand and the fingerprint of false finger can be identified, improve the anti-counterfeiting effect of fingerprint.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of intelligent terminal, in particular to a fingerprint anti-counterfeiting method, device and equipment. BACKGROUND

[0002] Fingerprint recognition is an important identity recognition method, compared with deoxyribo nucleic acid (DNA) recognition and iris recognition, has the characteristics of simple and easy to use, has been widely used in our life.

[0003] The fingerprint recognition scheme generally has the problem of weak anti-counterfeiting ability, there are many attack scenes in the existing related technology, and there is a great security risk.

[0004] Among them, the fake fingerprint attack is to collect the user's fingerprint through various means, and then make various fake fingerprints. Then use fake fingerprints to attack the user's fingerprint recognition device.

[0005] In the existing related technology, fingerprint recognition mainly classifies two by judging the image difference between true and false fingerprints, so as to judge whether the fingerprint is true or false. However, by comparing the difference between the images, only part of the fake fingerprints with large differences can be intercepted. If the difference between the true and false fingerprint images is small, the anti-counterfeiting effect of the fingerprint is poor. SUMMARY

[0006] Embodiments of the present application provide a fingerprint anti-counterfeiting method, device and equipment, and the embodiments of the present application also provide a computer readable storage medium to realize the identification of the fingerprint of a real human finger and the fingerprint of a fake finger, and improve the anti-counterfeiting effect of the fingerprint.

[0007] In a first aspect, the embodiments of the present application provide a fingerprint anti-counterfeiting method, comprising: collecting at least two frames of fingerprint images through a fingerprint sensor; performing matching identification on the at least two frames of fingerprint images; if the result of the matching identification is that the fingerprint in the at least two frames of fingerprint images matches the pre-stored fingerprint, performing anti-counterfeiting identification on the at least two frames of fingerprint images; if the result of the anti-counterfeiting identification is that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger, determining that the fingerprint in the at least two frames of fingerprint images passes the identity authentication.

[0008] In the fingerprint anti-counterfeiting method, the fingerprint anti-counterfeiting device collects at least two frames of fingerprint images through the fingerprint sensor, and then performs matching identification on the at least two frames of fingerprint images. If the matching identification result is that the fingerprint in the at least two frames of fingerprint images matches the pre-stored fingerprint, anti-counterfeiting identification is performed on the at least two frames of fingerprint images. If the anti-counterfeiting identification result is that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger, it is determined that the fingerprint in the at least two frames of fingerprint images passes the identity authentication. Thus, the fingerprint of a real human finger and the fingerprint of a fake finger can be identified, and the anti-counterfeiting effect of the fingerprint is improved.

[0009] In one possible implementation, after the anti-counterfeiting identification on the at least two frames of fingerprint images, the method further includes: if the anti-counterfeiting identification result is that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a fake finger, it is determined that the fingerprint in the at least two frames of fingerprint images fails the identity authentication.

[0010] In one possible implementation, the anti-counterfeiting identification on the at least two frames of fingerprint images includes: performing difference calculation on the at least two frames of fingerprint images to obtain a fingerprint difference sequence in the time domain; performing identification on the fingerprint difference sequence to obtain a confidence degree that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger; and determining whether the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger according to the confidence degree.

[0011] In one possible implementation, the identification on the fingerprint difference sequence to obtain the confidence degree that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger includes: performing identification on the fingerprint difference sequence through a neural network to obtain the confidence degree that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger.

[0012] In one possible implementation, the neural network includes a convolutional neural network and / or a fully connected network.

[0013] In one possible implementation, the anti-counterfeiting identification on the at least two frames of fingerprint images includes: performing difference calculation on the at least two frames of fingerprint images to obtain a fingerprint difference sequence in the time domain; fusing the fingerprint image and the fingerprint difference sequence; performing identification on a feature vector obtained after the fusion to obtain a confidence degree that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger; and determining whether the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger according to the confidence degree.

[0014] In a possible implementation, the determining whether the fingerprint in the at least two frames of fingerprint images is a real human finger according to the confidence level comprises: when the confidence level is greater than or equal to a predetermined threshold, determining that the fingerprint in the at least two frames of fingerprint images is a real human finger; and when the confidence level is less than the predetermined threshold, determining that the fingerprint in the at least two frames of fingerprint images is not a real human finger.

[0015] In a second aspect, an embodiment of the present application provides a fingerprint anti-counterfeiting device, which is included in a fingerprint anti-counterfeiting apparatus. The device has functions of implementing behaviors of the fingerprint anti-counterfeiting apparatus in the first aspect and possible implementation manners of the first aspect. The functions can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules or units corresponding to the above functions. For example, an acquisition module or unit, a processing module or unit, and the like.

[0016] In a third aspect, an embodiment of the present application provides a fingerprint anti-counterfeiting apparatus, comprising: one or more processors; a memory; a plurality of application programs; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the fingerprint anti-counterfeiting apparatus, cause the fingerprint anti-counterfeiting apparatus to perform the following steps: collecting at least two frames of fingerprint images through a fingerprint sensor; performing matching identification on the at least two frames of fingerprint images; if a result of the matching identification is that a fingerprint in the at least two frames of fingerprint images matches a pre-stored fingerprint, performing anti-counterfeiting identification on the at least two frames of fingerprint images; and if a result of the anti-counterfeiting identification is that the fingerprint in the at least two frames of fingerprint images is a real human finger, determining that the fingerprint in the at least two frames of fingerprint images passes identity authentication.

[0017] In a possible implementation, when the instructions are executed by the fingerprint anti-counterfeiting apparatus, the fingerprint anti-counterfeiting apparatus further performs the following step after performing the anti-counterfeiting identification on the at least two frames of fingerprint images: if a result of the anti-counterfeiting identification is that the fingerprint in the at least two frames of fingerprint images is a fake finger, determining that the fingerprint in the at least two frames of fingerprint images fails identity authentication.

[0018] In a possible implementation, when the instructions are executed by the fingerprint anti-counterfeiting apparatus, the fingerprint anti-counterfeiting apparatus performs the anti-counterfeiting identification on the at least two frames of fingerprint images by performing difference calculation on the at least two frames of fingerprint images to obtain a fingerprint difference sequence in a time domain, performing identification on the fingerprint difference sequence to obtain a confidence level that the fingerprint in the at least two frames of fingerprint images is a real human finger, and determining whether the fingerprint in the at least two frames of fingerprint images is a real human finger according to the confidence level.

[0019] In a possible implementation, when the instructions are executed by the fingerprint anti-counterfeiting device, the fingerprint anti-counterfeiting device is caused to perform the step of identifying the fingerprint difference sequence to obtain the confidence that the fingerprint in the at least two fingerprint images is a real human finger.

[0020] In a possible implementation, when the instructions are executed by the fingerprint anti-counterfeiting device, the fingerprint anti-counterfeiting device is caused to perform the step of identifying the fingerprint difference sequence to obtain the confidence that the fingerprint in the at least two fingerprint images is a real human finger.

[0021] In a possible implementation, when the instructions are executed by the fingerprint anti-counterfeiting device, the fingerprint anti-counterfeiting device is caused to perform the step of identifying the fingerprint difference sequence to obtain the confidence that the fingerprint in the at least two fingerprint images is a real human finger.

[0022] It should be understood that the second to third aspects of the embodiments of the present application are consistent with the technical solutions of the first aspect of the embodiments of the present application, and the beneficial effects obtained by the aspects and corresponding feasible implementation manners are similar, which will not be described herein again.

[0023] In a possible implementation, when the instructions are executed by the fingerprint anti-counterfeiting device, the fingerprint anti-counterfeiting device is caused to perform the step of identifying the fingerprint difference sequence to obtain the confidence that the fingerprint in the at least two fingerprint images is a real human finger.

[0024] In a possible implementation, when the instructions are executed by the fingerprint anti-counterfeiting device, the fingerprint anti-counterfeiting device is caused to perform the step of identifying the fingerprint difference sequence to obtain the confidence that the fingerprint in the at least two fingerprint images is a real human finger.

[0025] In a possible design, the program in the fifth aspect can be stored in a storage medium packaged together with the processor in whole or in part, or stored in a storage medium not packaged together with the processor in whole or in part. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1A signal diagram of the fingerprint images of the real and fake fingers provided by an embodiment of the present application;

[0027] Figure 2 A structural diagram of the fingerprint anti-counterfeiting device provided by an embodiment of the present application;

[0028] Figure 3 A flowchart of the fingerprint anti-counterfeiting method provided by an embodiment of the present application;

[0029] Figure 4 A flowchart of the fingerprint anti-counterfeiting method provided by another embodiment of the present application;

[0030] Figure 5 A flowchart of the fingerprint anti-counterfeiting method provided by another embodiment of the present application;

[0031] Figure 6 A structural diagram of the fingerprint anti-counterfeiting device provided by another embodiment of the present application. DETAILED DESCRIPTION

[0032] The terms used in the embodiment part of the present application are only used for explaining the specific embodiments of the present application, and are not intended to limit the present application.

[0033] Based on the problem of poor anti-counterfeiting effect of the fingerprint in the prior art, the fingerprint anti-counterfeiting method provided by the embodiments of the present application can identify the fingerprints of the real human fingers and the fake fingers, and improve the anti-counterfeiting effect of the fingerprint.

[0034] Since the state change of the fake finger is small, and the state change of the real human finger is large due to reasons such as sweat and / or blood flow caused by the contraction of pores, the signal size change and the differential signal distribution in the time sequence of the fingerprint images can be used to distinguish the real and fake fingers.

[0035] Figure 1 A signal diagram of the fingerprint images of the real and fake fingers provided by an embodiment of the present application, Figure 1 The signals shown are the signals in the time sequence of the fingerprint images of the real and fake fingers. From Figure 1 It can be seen from the signal size change that the signal size change of the fake finger is small, and the signal size change of the real human finger is large.

[0036] In addition, from the differential signal distribution diagram in the time sequence of the fingerprint images of the real and fake fingers, it can also be seen that the differential signal change distribution of the fake finger is concentrated (mainly caused by the local finger force change), and the differential signal change distribution of the real human finger is dispersed (mainly caused by reasons such as sweat and / or blood flow caused by the contraction of pores).

[0037] The fingerprint anti-counterfeiting method provided by the embodiments of the present application can be implemented by a fingerprint anti-counterfeiting device. The fingerprint anti-counterfeiting device can be applied to an electronic device. The electronic device can be a fingerprint clock-in machine, a smart phone, a tablet computer, a wearable device, a vehicle-mounted device, an augmented reality (AR) / virtual reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), or the like, which requires fingerprint authentication. The embodiments of the present application do not limit the specific type of the electronic device.

[0038] Exemplarily, Figure 2 A structural schematic diagram of a fingerprint anti-counterfeiting device provided by an embodiment of the present application is shown in FIG. 2. As shown in FIG. 2, the fingerprint anti-counterfeiting device 200 can include a processor 201, a display screen 202, a fingerprint sensor 203, and a touch sensor 204. Figure 2

[0039] In addition, the fingerprint anti-counterfeiting device 200 can further include an internal memory 205.

[0040] It can be understood that the structure shown in the embodiments of the present application does not constitute a specific limitation on the fingerprint anti-counterfeiting device 200. In other embodiments of the present application, the fingerprint anti-counterfeiting device 200 can include more or fewer components than shown in the figure, or combine certain components, or split certain components, or different component arrangements. Figure 2 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0041] The processor 201 can include one or more processing units. For example, the processor 201 can include an application processor (AP), a graphics processing unit (GPU), an image signal processor (ISP), a controller, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units can be independent devices or integrated into one or more processors.

[0042] The controller can generate operation control signals according to instruction operation codes and timing signals to complete the control of fetching and executing instructions.

[0043] ​The processor 201 can also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 201 is a cache memory. The memory can hold instructions or data that the processor 201 has just used or recycled. If the processor 201 needs to use the instructions or data again, it can be directly called from the memory. This avoids repeated access and reduces the waiting time of the processor 201, thus improving the efficiency of the system.

[0044] The display screen 202 is used to display images, videos, etc. The display screen 202 includes a display panel. The display panel can adopt a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light emitting diode (AMOLED), a flex light-emitting diode (FLED), a Miniled, a MicroLed, a Micro-oLed, a quantum dot light emitting diode (QLED), etc. In some embodiments, the fingerprint anti-counterfeiting device 200 can include one or N display screens 202, and N is a positive integer greater than 1.

[0045] The digital signal processor is used to process digital signals, which can process not only digital image signals but also other digital signals. For example, when the fingerprint anti-counterfeiting device 200 selects a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy, etc.

[0046] The NPU is a neural-network (NN) computing processor, which can quickly process input information by drawing on the structure of a biological neural network, such as drawing on the transmission mode between human brain neurons, and can also constantly self-learn. Through the NPU, intelligent cognitive applications of the fingerprint anti-counterfeiting device 200 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

[0047] The internal memory 205 can be used to store computer executable program codes including instructions. The internal memory 205 can include a program storage area and a data storage area. The program storage area can store an operating system, at least one application required by a function (such as a fingerprint identification function), and the like. The data storage area can store data (such as fingerprint data) created during use of the fingerprint anti-counterfeiting device 200, and the like. In addition, the internal memory 205 can include a high-speed random access memory, and can also include a non-volatile memory such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), and the like. The processor 201 executes various function applications and data processing of the fingerprint anti-counterfeiting device 200 by running instructions stored in the internal memory 205 and / or instructions stored in a memory disposed in the processor.

[0048] The fingerprint sensor 203 is used to collect fingerprints. The fingerprint anti-counterfeiting device 200 can use the collected fingerprint characteristics to realize fingerprint unlocking, access application locking, fingerprint photographing, fingerprint answering a call, and the like.

[0049] The touch sensor 204 is also referred to as a "touch device". The touch sensor 204 can be disposed on the display screen 202, and the touch sensor 204 and the display screen 202 form a touch screen, also referred to as a "touch screen". The touch sensor 204 is used to detect a touch operation acting on or near the touch sensor 204. The touch sensor 204 can transmit the detected touch operation to the application processor to determine a touch event type. Visual output related to the touch operation can be provided through the display screen 202. In other embodiments, the touch sensor 204 can also be disposed on a surface of the fingerprint anti-counterfeiting device 200, which is different from the position of the display screen 202.

[0050] For ease of understanding, the following embodiments of the present application will take the fingerprint anti-counterfeiting device 200 having the structure shown in Figure 2 The fingerprint anti-counterfeiting method provided by the embodiments of the present application will be specifically described in combination with the drawings and application scenarios, taking the fingerprint anti-counterfeiting device 200 having the structure shown in

[0051] Figure 3 The flowchart of the fingerprint anti-counterfeiting method provided by one embodiment of the present application is shown in Figure 3 The fingerprint anti-counterfeiting method can include the following steps.

[0052] In step 301, the fingerprint anti-counterfeiting device 200 collects at least two frames of fingerprint images through the fingerprint sensor 203.

[0053] In a specific implementation, the fingerprint hardware gain and other parameters of the fingerprint sensor 203 can be fixed to ensure the consistency of the signals output by the fingerprint sensor 203 each time. Then, the fingerprint sensor 203 can continuously collect at least two frames of fingerprint images, and the fingerprint anti-counterfeiting device 200 acquires the at least two frames of fingerprint images.

[0054] At step 302, the at least two frames of fingerprint images are matched and identified. Then, step 303 or step 306 is performed.

[0055] At step 303, if the matching and identification result is that the fingerprints in the at least two frames of fingerprint images match the pre-stored fingerprints, the at least two frames of fingerprint images are subjected to anti-counterfeiting identification. Then, step 304 or step 305 is performed.

[0056] At step 304, if the anti-counterfeiting identification result is that the fingerprints in the at least two frames of fingerprint images are fingerprints of real human fingers, it is determined that the fingerprints in the at least two frames of fingerprint images pass the identity authentication.

[0057] At step 305, if the anti-counterfeiting identification result is that the fingerprints in the at least two frames of fingerprint images are fingerprints of fake fingers, it is determined that the fingerprints in the at least two frames of fingerprint images fail the identity authentication.

[0058] At step 306, if the matching and identification result is that the fingerprints in the at least two frames of fingerprint images do not match the pre-stored fingerprints, it is determined that the fingerprints in the at least two frames of fingerprint images fail the identity authentication.

[0059] Specifically, after the fingerprint anti-counterfeiting device 200 acquires the at least two frames of fingerprint images, a fingerprint matching algorithm is first run to match and identify the at least two frames of fingerprint images. If the fingerprints in the at least two frames of fingerprint images match the pre-stored fingerprints, the at least two frames of fingerprint images are subjected to anti-counterfeiting identification. If the anti-counterfeiting identification result is a fingerprint of a real human finger, it is determined that the matching is successful, and the fingerprints in the at least two frames of fingerprint images pass the current identity authentication.

[0060] If the fingerprints in the at least two frames of fingerprint images do not match the pre-stored fingerprints, or the fingerprints in the at least two frames of fingerprint images match the pre-stored fingerprints but the anti-counterfeiting identification result is a fingerprint of a fake finger, it is determined that the matching fails, and the fingerprints in the at least two frames of fingerprint images fail the current identity authentication.

[0061] In the fingerprint anti-counterfeiting method, the fingerprint anti-counterfeiting device 200 collects at least two frames of fingerprint images through the fingerprint sensor 203, and then performs matching identification on the at least two frames of fingerprint images. If the matching identification result is that the fingerprint in the at least two frames of fingerprint images matches the pre-stored fingerprint, anti-counterfeiting identification is performed on the at least two frames of fingerprint images. If the anti-counterfeiting identification result is that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger, it is determined that the fingerprint in the at least two frames of fingerprint images passes the identity authentication. Thus, the fingerprint of a real human finger and the fingerprint of a fake finger can be identified, and the anti-counterfeiting effect of the fingerprint is improved.

[0062] Figure 4 The flowchart of the fingerprint anti-counterfeiting method provided for another embodiment of the present specification is shown in FIG. 3, and the fingerprint anti-counterfeiting method provided for the embodiment shown in FIG. 3 can include the following steps. Figure 4 Figure 3 In the embodiment shown in FIG. 3, step 303 can include the following steps.

[0063] Step 401, if the matching identification result is that the fingerprint in the at least two frames of fingerprint images matches the pre-stored fingerprint, difference calculation is performed on the at least two frames of fingerprint images to obtain a time-domain fingerprint difference sequence.

[0064] Step 402, identification is performed on the fingerprint difference sequence to obtain a confidence degree that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger.

[0065] Specifically, the identification of the fingerprint difference sequence to obtain the confidence degree that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger can be that the confidence degree that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger is obtained by identifying the fingerprint difference sequence through a neural network.

[0066] The neural network can include a convolutional neural network and / or a fully connected network.

[0067] Step 403, whether the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger is determined according to the confidence degree.

[0068] Specifically, whether the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger can be determined according to the confidence degree as follows: when the confidence degree is greater than or equal to a predetermined threshold, it is determined that the fingerprint in the at least two frames of fingerprint images is the fingerprint of a real human finger; and when the confidence degree is less than the predetermined threshold, it is determined that the fingerprint in the at least two frames of fingerprint images is not the fingerprint of a real human finger.

[0069] The predetermined threshold can be set according to system performance and / or implementation requirements in specific implementation, and the size of the predetermined threshold is not limited in the embodiment.

[0070] ​The embodiment utilizes the fact that the fingerprint of the fake finger and the real finger is different when pressed, the signal of the fake finger changes less over time, and the differential signal change distribution is concentrated, while the real finger changes more over time due to reasons such as sweat pores shrinking, sweating and / or blood flow, and the differential signal change distribution is dispersed, so that the fingerprint difference sequence is recognized to obtain the confidence, and then whether the fingerprint in the fingerprint image is the fingerprint of the real finger is determined according to the confidence, so that the fingerprint of the real finger and the fingerprint of the fake finger can be recognized, and the anti-counterfeiting effect of the fingerprint is improved.

[0071] Figure 5 The flowchart of the fingerprint anti-counterfeiting method provided for another embodiment of the present specification is shown in Figure 5 The embodiment shown in the present specification Figure 3 In the embodiment shown in the present specification, step 303 can include:

[0072] Step 501, if the matching recognition result is that the fingerprint in at least two frames of fingerprint images matches the pre-stored fingerprint, then the at least two frames of fingerprint images are subjected to differential calculation to obtain a time-domain fingerprint difference sequence.

[0073] Step 502, fuse the above-mentioned fingerprint image and the above-mentioned fingerprint difference sequence.

[0074] Step 503, recognize the feature vector obtained after fusion to obtain the confidence that the fingerprint in at least two frames of fingerprint images is the fingerprint of the real finger.

[0075] Specifically, recognizing the feature vector obtained after fusion to obtain the confidence that the fingerprint in at least two frames of fingerprint images is the fingerprint of the real finger can be: recognizing the feature vector obtained after fusion through a neural network to obtain the confidence that the fingerprint in at least two frames of fingerprint images is the fingerprint of the real finger.

[0076] The above-mentioned neural network can include a convolutional neural network and / or a fully connected network.

[0077] Step 504, determine whether the fingerprint in at least two frames of fingerprint images is the fingerprint of the real finger according to the above-mentioned confidence.

[0078] Specifically, determining whether the fingerprint in at least two frames of fingerprint images is the fingerprint of the real finger according to the above-mentioned confidence can be: when the above-mentioned confidence is greater than or equal to a predetermined threshold, determining that the fingerprint in at least two frames of fingerprint images is the fingerprint of the real finger; and when the above-mentioned confidence is less than the predetermined threshold, determining that the fingerprint in at least two frames of fingerprint images is not the fingerprint of the real finger.

[0079] The predetermined threshold can be set according to system performance and / or implementation requirements in specific implementation, and the embodiment does not limit the size of the predetermined threshold.

[0080] The embodiment utilizes the fact that the fingerprint of the fake finger and the real finger is different when pressed, the signal of the fake finger changes less over time, and the differential signal change distribution is concentrated, while the real finger has sweat pores shrinkage, sweating and / or blood flow, etc., resulting in a large signal change over time, and the differential signal change distribution is dispersed. Therefore, the embodiment performs differential calculation on at least two frames of fingerprint images to obtain a time-domain fingerprint differential sequence, then fuses the fingerprint image and the fingerprint differential sequence, and further identifies the feature vector obtained after fusion to obtain a confidence level, and then determines whether the fingerprint in the fingerprint image is the fingerprint of the real finger according to the confidence level, so that the fingerprint of the real finger and the fingerprint of the fake finger can be identified, and the anti-counterfeiting effect of the fingerprint is improved.

[0081] It can be understood that part or all of the steps or operations in the above embodiments are only examples, and the embodiments of the present application can also perform other operations or variations of various operations. In addition, each step can be executed in a different order from the above-described embodiments, and it is possible that not all the operations in the above-described embodiments are executed.

[0082] It can be understood that in order to realize the above functions, the fingerprint anti-counterfeiting device includes corresponding hardware and / or software modules for executing each function. The algorithm steps of each example described in combination with the embodiments disclosed in the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in hardware or computer software driven hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in combination with the embodiments, but such implementation should not be considered beyond the scope of the present application.

[0083] The embodiment can divide the fingerprint anti-counterfeiting device into functional modules according to the method embodiments described above. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one module. The integrated module can be implemented in the form of hardware. It should be noted that the division of modules in the embodiment is illustrative, and is only a logical functional division. There can be another division method when actually implemented.

[0084] Figure 6 The structure schematic diagram of the fingerprint anti-counterfeiting device provided for another embodiment of the present application is shown in the case of dividing each functional module according to each function, Figure 6 A possible composition schematic diagram of the fingerprint anti-counterfeiting device 60 involved in the above embodiment is shown as follows,Figure 6 As shown in the figure, the fingerprint anti-counterfeiting device 60 can include an acquisition unit 61 and a processing unit 62.

[0085] The acquisition unit 61 can be configured to support the fingerprint anti-counterfeiting device 60 to perform steps 301 and the like, and / or other processes of the technical solutions described in the embodiments of the present application.

[0086] The processing unit 62 can be configured to support the fingerprint anti-counterfeiting device 60 to perform steps 302-306, steps 401-403, steps 501-504, and the like, and / or other processes of the technical solutions described in the embodiments of the present application.

[0087] It should be noted that all related content of each step involved in the above method embodiments can be cited to the function description of the corresponding function module, which will not be repeated here.

[0088] The fingerprint anti-counterfeiting device 60 provided in the embodiment is configured to perform the fingerprint anti-counterfeiting method described above, and thus can achieve the same effect as the above method.

[0089] It should be understood that the fingerprint anti-counterfeiting device 60 can correspond to Figure 1 The fingerprint anti-counterfeiting device 200 as shown in the figure. The function of the acquisition unit 61 can be implemented by the fingerprint sensor 203 and the touch sensor 204 in the fingerprint anti-counterfeiting device 200 as shown in the figure. Figure 2 The function of the processing unit 62 can be implemented by the processor 201 in the fingerprint anti-counterfeiting device 200 as shown in the figure. Figure 2

[0090] In the case of using integrated units, the fingerprint anti-counterfeiting device 60 can include a processing module and a storage module.

[0091] The processing module can be configured to control and manage the actions of the fingerprint anti-counterfeiting device 60, for example, it can be configured to support the fingerprint anti-counterfeiting device 60 to perform the steps performed by the acquisition unit 61 and the processing unit 62 described above. The storage module can be configured to support the fingerprint anti-counterfeiting device 60 to store program codes and data and the like.

[0092] The processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, digital signal processing (DSP) and microprocessor combinations, and the like. The storage module can be a memory. The communication module can be a radio frequency circuit, a Bluetooth chip, and / or a Wi-Fi chip, and the like, which interacts with other electronic devices.

[0093] ​In one embodiment, when the processing module is a processor and the storage module is a memory, the fingerprint anti-counterfeiting device 60 can be a device with the structure as shown in the figure. Figure 2

[0094] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and when the computer program is run on a computer, the computer is enabled to execute the method provided by the embodiments of the present application. Figures 3-5

[0095] The embodiments of the present application also provide a computer program product, which includes a computer program, and when the computer program is run on a computer, the computer is enabled to execute the method provided by the embodiments of the present application. Figures 3-5

[0096] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. The character " / " generally represents that the associated objects before and after it are in an "or" relationship. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, wherein a, b, and c can be single or multiple.

[0097] Those skilled in the art can realize that the units and algorithm steps described in the embodiments disclosed in the present application can be realized by electronic hardware, computer software and combination of electronic hardware and computer software. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0099] ​​​In several embodiments provided in the present application, any function, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, in essence or the parts that make contributions to the prior art, or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0100] The above description is merely a specific implementation of the present application. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application. The protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of fingerprint forgery prevention, characterized by, comprising: acquiring at least two frames of fingerprint images through a fingerprint sensor; performing matching identification on the at least two frames of fingerprint images; if a result of the matching identification is that a fingerprint in the at least two frames of fingerprint images matches a pre-stored fingerprint, performing anti-fake identification on the at least two frames of fingerprint images; if a result of the anti-fake identification is that the fingerprint in the at least two frames of fingerprint images is a fingerprint of a real human finger, determining that the fingerprint in the at least two frames of fingerprint images passes identity authentication; wherein the anti-fake identification on the at least two frames of fingerprint images comprises: performing difference calculation on the at least two frames of fingerprint images to obtain a fingerprint difference sequence in a time domain; fusing the fingerprint images and the fingerprint difference sequence; performing identification on a feature vector obtained after the fusing to obtain a confidence degree that the fingerprint in the at least two frames of fingerprint images is a fingerprint of a real human finger; determining, according to the confidence degree, whether the fingerprint in the at least two frames of fingerprint images is a fingerprint of a real human finger; wherein the identification on the feature vector obtained after the fusing to obtain the confidence degree that the fingerprint in the at least two frames of fingerprint images is a fingerprint of a real human finger comprises: performing identification on the feature vector obtained after the fusing through a neural network to obtain the confidence degree that the fingerprint in the at least two frames of fingerprint images is a fingerprint of a real human finger; the neural network comprises a convolutional neural network and / or a fully connected network; the determination according to the confidence degree that the fingerprint in the at least two frames of fingerprint images is a fingerprint of a real human finger comprises: when the confidence degree is greater than or equal to a predetermined threshold, determining that the fingerprint in the at least two frames of fingerprint images is a fingerprint of a real human finger; when the confidence degree is less than the predetermined threshold, determining that the fingerprint in the at least two frames of fingerprint images is not a fingerprint of a real human finger.

2. The method of claim 1, wherein, after the anti-fake identification on the at least two frames of fingerprint images, further comprising: if a result of the anti-fake identification is that the fingerprint in the at least two frames of fingerprint images is a fake finger, determining that the fingerprint in the at least two frames of fingerprint images does not pass identity authentication.

3. A fingerprint anti-spoofing device, characterized by, comprising: one or more processors; a memory; a plurality of application programs; and one or more computer programs, wherein the one or more computer programs are stored in the memory, the one or more computer programs comprise instructions, when the instructions are executed by the fingerprint anti-fake device, causing the fingerprint anti-fake device to perform the following steps: acquiring at least two frames of fingerprint images through a fingerprint sensor; performing matching identification on the at least two frames of fingerprint images; if a result of the matching identification is that a fingerprint in the at least two frames of fingerprint images matches a pre-stored fingerprint, performing anti-fake identification on the at least two frames of fingerprint images; if a result of the anti-fake identification is that the fingerprint in the at least two frames of fingerprint images is a fingerprint of a real human finger, determining that the fingerprint in the at least two frames of fingerprint images passes identity authentication; when the instructions are executed by the fingerprint anti-fake device, causing the fingerprint anti-fake device to perform the steps of the anti-fake identification on the at least two frames of fingerprint images comprises: performing difference calculation on the at least two frames of fingerprint images to obtain a fingerprint difference sequence in a time domain; fusing the fingerprint image and the fingerprint difference sequence; performing recognition on the feature vector obtained after fusion to obtain a confidence degree of the fingerprint in the at least two frames of fingerprint images being a real human finger fingerprint; determining, according to the confidence degree, whether the fingerprint in the at least two frames of fingerprint images is a real human finger fingerprint; wherein when the instructions are executed by the fingerprint anti-counterfeiting device, the fingerprint anti-counterfeiting device performs the step of performing recognition on the feature vector obtained after fusion to obtain a confidence degree of the fingerprint in the at least two frames of fingerprint images being a real human finger fingerprint, including: performing recognition on the feature vector obtained after fusion by a neural network to obtain a confidence degree of the fingerprint in the at least two frames of fingerprint images being a real human finger fingerprint; the neural network includes a convolutional neural network and / or a fully connected network; when the instructions are executed by the fingerprint anti-counterfeiting device, the fingerprint anti-counterfeiting device performs the step of determining, according to the confidence degree, whether the fingerprint in the at least two frames of fingerprint images is a real human finger fingerprint, including: when the confidence degree is greater than or equal to a predetermined threshold, determining that the fingerprint in the at least two frames of fingerprint images is a real human finger fingerprint; when the confidence degree is less than the predetermined threshold, determining that the fingerprint in the at least two frames of fingerprint images is not a real human finger fingerprint.

4. The fingerprint security device according to claim 3, characterized in that when the instructions are executed by the fingerprint anti-counterfeiting device, the fingerprint anti-counterfeiting device performs the step of performing anti-counterfeiting identification on the at least two frames of fingerprint images, and further performs the following steps: if the result of the anti-counterfeiting identification is that the fingerprint in the at least two frames of fingerprint images is a fake finger fingerprint, determining that the fingerprint in the at least two frames of fingerprint images fails the identity authentication.

5. A computer readable storage medium, characterized in that, The computer readable storage medium stores a computer program, which, when running on a computer, causes the computer to perform the method of any one of claims 1-2. The computer readable storage medium stores a computer program, which, when running on a computer, causes the computer to perform the method of any one of claims 1-2.

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