An identity authentication method, device, storage device and electronic equipment

Abstract

The embodiment of the present specification discloses a kind of identity authentication method, no longer based on the fingerprint locally saved by mobile terminal to the user for identity authentication, but after the standard fingerprint image of the user for identity authentication based on the standard fingerprint image of the authority that is collected by contact type collection method is collected by non-contact collection method, the gap of the fingerprint collected by two different methods in application is broken, and the authentication credibility of mobile terminal is improved.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and in particular to a method, apparatus, storage device, and electronic device for identity authentication. Background Technology

[0002] Currently, methods for identity authentication using biometric information, such as fingerprints, have been widely applied in various fields. The following explanation uses fingerprints as an example.

[0003] Many mobile devices, such as smartphones and tablets, have fingerprint recognition functionality, which is typically used for unlocking devices and making payments. The principle behind this fingerprint recognition is usually as follows: the mobile device uses its hardware and software to collect the user's fingerprint and stores it locally. When identifying a user, it uses the locally stored fingerprint.

[0004] Users' fingerprints are also stored in authoritative institutions in other scenarios. These authoritative institutions often collect and store fingerprints through professional contact devices. For example, when users apply for an ID card or conduct certain financial transactions at a bank, they may need to have their fingerprints collected through professional contact fingerprint collection devices and stored in authoritative institutions.

[0005] It is evident that there is a natural gap between the contactless fingerprint collection method used by mobile terminals and the contact fingerprint collection method used by authoritative institutions. When performing fingerprint recognition, mobile terminals cannot use fingerprints stored in authoritative institutions to identify users, resulting in lower authentication credibility of mobile terminals. Summary of the Invention

[0006] This specification provides an identity authentication method, apparatus, storage device, and electronic device to partially solve the problems existing in the prior art.

[0007] The embodiments in this specification adopt the following technical solutions:

[0008] This specification provides a method for identity authentication, the method comprising:

[0009] The user's fingerprint image to be authenticated is collected using a non-contact acquisition method;

[0010] Extract the fingerprint features to be authenticated from the fingerprint image to be authenticated;

[0011] The user is authenticated based on the fingerprint features to be authenticated and the standard fingerprint features extracted from a pre-saved standard fingerprint image, wherein the standard fingerprint image is a fingerprint image pre-saved in an authoritative institution and collected by a contact acquisition method.

[0012] Optionally, the user's fingerprint image to be authenticated is acquired using a contactless acquisition method, specifically including:

[0013] The system uses a camera to capture the user's fingerprint image to be authenticated.

[0014] Optionally, extracting fingerprint features to be authenticated from the fingerprint image to be authenticated specifically includes:

[0015] The environmental features acquired during the collection of the fingerprint image to be authenticated include at least one of light features and background noise features.

[0016] Based on the environmental characteristics, determine the image quality characterization value of the fingerprint image to be authenticated;

[0017] If the image quality characterization value meets the preset conditions, then the fingerprint features to be authenticated are extracted from the fingerprint image to be authenticated.

[0018] Optionally, based on the environmental characteristics, the image quality characterization value of the fingerprint image to be authenticated is determined, specifically including:

[0019] The environmental features and the fingerprint image to be authenticated are input into a pre-trained image quality evaluation model so that the image quality evaluation model can determine the texture sharpness quality and the degree of deflection of the fingerprint image to be authenticated, and obtain the image quality characterization value of the fingerprint image to be authenticated based on the texture sharpness quality and the degree of deflection.

[0020] Optionally, extracting fingerprint features to be authenticated from the fingerprint image to be authenticated specifically includes:

[0021] Perform liveness detection on the fingerprint image to be authenticated;

[0022] If the liveness detection passes, the fingerprint features to be authenticated are extracted from the fingerprint image to be authenticated.

[0023] Optionally, extracting fingerprint features to be authenticated from the fingerprint image to be authenticated specifically includes:

[0024] The fingerprint image to be authenticated is input into a pre-trained fingerprint feature extraction model to extract the machine learning fingerprint features to be authenticated from the fingerprint image to be authenticated; and the MCC fingerprint features to be authenticated are extracted from the fingerprint image to be authenticated.

[0025] The machine learning fingerprint features to be authenticated and the MCC fingerprint features to be authenticated are combined to form the fingerprint features to be authenticated.

[0026] Extracting standard fingerprint features from pre-saved standard fingerprint images, specifically including:

[0027] The standard fingerprint image is input into a pre-trained fingerprint feature extraction model to extract standard machine learning fingerprint features from the standard fingerprint image; and standard MCC fingerprint features are extracted from the standard fingerprint image.

[0028] The standard machine learning fingerprint features and the standard MCC fingerprint features are combined to form the standard fingerprint features.

[0029] Optionally, a fingerprint feature extraction model is pre-trained, specifically including:

[0030] The sample fingerprint image and the sample standard fingerprint image of the sample user are acquired. The sample fingerprint image is acquired by a non-contact acquisition method, and the sample standard fingerprint image is acquired by a contact acquisition method.

[0031] Using the fingerprint feature extraction model to be trained, sample machine learning fingerprint features are extracted from the sample fingerprint image, and sample standard machine learning fingerprint features are extracted from the sample standard fingerprint image; and sample MCC fingerprint features are extracted from the sample fingerprint image, and sample standard MCC fingerprint features are extracted from the sample standard fingerprint image.

[0032] The sample fingerprint features are obtained by fusing the sample machine learning fingerprint features and the sample MCC fingerprint features; the sample standard fingerprint features are obtained by fusing the sample standard machine learning fingerprint features and the sample standard MCC fingerprint features.

[0033] The loss value is calculated based on the sample fingerprint features, the sample standard fingerprint features, and the preset similarity loss function;

[0034] The fingerprint feature extraction model to be trained is trained based on the loss value.

[0035] This specification provides an identity authentication system, which includes: a terminal and a remote server;

[0036] The terminal acquires the user's fingerprint image to be authenticated using a non-contact acquisition method and sends the fingerprint image to the remote server.

[0037] The remote server extracts fingerprint features to be authenticated from the fingerprint image to be authenticated; and authenticates the user's identity based on the fingerprint features to be authenticated and the standard fingerprint features extracted from a pre-saved standard fingerprint image, wherein the standard fingerprint image is a fingerprint image pre-saved in an authoritative institution and collected by a contact acquisition method.

[0038] This specification provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned authentication method.

[0039] This specification provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the aforementioned authentication method.

[0040] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects:

[0041] This specification discloses an identity authentication method that no longer relies on fingerprints stored locally on the mobile terminal for user authentication. Instead, it uses a non-contact acquisition method to acquire the image to be authenticated, and then uses a standard fingerprint image acquired by an authoritative institution through a contact acquisition method to authenticate the user. This breaks down the barriers between fingerprints acquired by two different methods in application and improves the authentication credibility of the mobile terminal. Attached Figure Description

[0042] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings:

[0043] Figure 1 A flowchart illustrating an identity authentication method provided in an embodiment of this specification;

[0044] Figure 2 This is a schematic diagram illustrating the display of prompt information on a mobile terminal provided in the embodiments of this specification;

[0045] Figure 3 This specification provides a schematic diagram of an identity authentication system architecture as illustrated in an embodiment.

[0046] Figure 4 A schematic diagram of an identity authentication device provided in the embodiments of this specification;

[0047] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this specification. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0049] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0050] When processing transactions requiring strict identity verification, such as ID card verification, large-amount transfers, and bank account openings, users often need to visit designated institutions in person. This is because, although mobile devices can perform fingerprint authentication, the principle behind this is that fingerprint authentication relies on fingerprints collected by the mobile device using its own non-professional collection equipment and methods, resulting in low reliability. Therefore, if these transactions with strict identity verification requirements are moved online, mobile devices would need to perform fingerprint authentication. Even if the mobile device successfully authenticates the user's identity using this fingerprint authentication function, the low reliability of the authentication would pose significant risks to these transactions.

[0051] Therefore, this specification provides an identity authentication method through its embodiments.

[0052] Figure 1 This is a flowchart illustrating one authentication method described in this specification, which may specifically include the following steps:

[0053] S100: Collects the user's fingerprint image to be authenticated using a non-contact acquisition method.

[0054] In the embodiments described in this specification, the system for implementing the authentication method may include a mobile terminal (e.g., a mobile phone, tablet computer, etc.), or it may include both a mobile terminal and a remote server. The following describes an implementation using a mobile terminal and a remote server. Figure 1 The following is an example of the identity authentication method shown.

[0055] Mobile terminals can collect users' fingerprint images using contactless acquisition methods as fingerprint images to be authenticated. Specifically, the contactless acquisition method described in this specification refers to a method that collects fingerprint images without touching the user's finger. This contactless acquisition method does not use any fingerprint scanner and can directly collect the user's fingerprint image to be authenticated through the mobile terminal's own camera.

[0056] After the mobile terminal collects the fingerprint image to be authenticated, it sends the fingerprint image to the remote server.

[0057] S102: Extract the fingerprint features to be authenticated from the fingerprint image to be authenticated.

[0058] Once the remote server receives the fingerprint image to be authenticated from the mobile terminal, it can extract the fingerprint features from the fingerprint image and use them as the fingerprint features to be authenticated.

[0059] It should be noted that, in this specification, the remote server can extract fingerprint features (hereinafter referred to as the machine learning fingerprint features to be authenticated) from the fingerprint image to be authenticated using a pre-trained fingerprint feature extraction model. To further improve the credibility of identity authentication, the fingerprint features to be authenticated described in this specification can be a fusion feature that combines the machine learning fingerprint features to be authenticated with the Minutia Cylinder-Code (MCC) fingerprint features to be authenticated.

[0060] Specifically, the remote server can input the fingerprint image to be authenticated into a pre-trained fingerprint feature extraction model. The model then extracts machine learning fingerprint features and MCC fingerprint features from the fingerprint image, and fuses these two features to obtain the final authenticated fingerprint. The extraction of machine learning fingerprint features and MCC fingerprint features is not performed in any particular order.

[0061] When fusing the machine learning fingerprint features and the MCC fingerprint features to be authenticated, the dimensions of the machine learning fingerprint features and the MCC fingerprint features to be authenticated can be adjusted to the same dimension first, and then fused using max pooling, min pooling, or average pooling methods. Alternatively, the machine learning fingerprint features and the MCC fingerprint features to be authenticated can be directly concatenated (concat) to obtain the fused fingerprint features. Those skilled in the art should understand that any method used to fuse the machine learning fingerprint features and the MCC fingerprint features to be authenticated can achieve the authentication method described in this specification.

[0062] The reason why this manual does not use machine learning fingerprint features or MCC fingerprint features alone, but instead combines the two, is that: when extracting MCC fingerprint features, it is necessary to extract them from fingerprint images collected using a professional contact fingerprint scanner. The reliability of extracting MCC fingerprint features from fingerprint images collected using non-contact acquisition methods is not high, and the interpretability of machine learning fingerprint features is not very strong. Therefore, in order to maximize the credibility of identity authentication, this manual combines the two features as the basis for identity authentication.

[0063] S104: Authenticate the user's identity based on the fingerprint features to be authenticated and the standard fingerprint features extracted from a pre-saved standard fingerprint image.

[0064] The standard fingerprint image is a fingerprint image pre-stored in an authoritative institution and acquired through a contact-based acquisition method. In other words, the standard fingerprint image is a fingerprint image acquired using a contact fingerprint scanner and stored in an authoritative institution.

[0065] In this embodiment of the specification, the remote server also needs to extract standard fingerprint features from the pre-saved standard fingerprint image. Similar to step S102 above, the extracted standard fingerprint features are also fused features that combine standard machine learning fingerprint features and standard MCC fingerprint features. The remote server needs to use the same fingerprint feature extraction model to extract standard machine learning fingerprint features.

[0066] Specifically, the remote server can first obtain the user's standard fingerprint image stored in an authoritative institution, input the standard fingerprint image into a pre-trained fingerprint feature extraction model, and then extract standard machine learning fingerprint features from the standard fingerprint image and standard MCC fingerprint features from the standard fingerprint image. The standard machine learning fingerprint features and the standard MCC fingerprint features are then fused together to form the standard fingerprint feature. The order of extracting the standard machine learning fingerprint features and extracting the standard MCC fingerprint features is not important. The method for fusing the standard machine learning fingerprint features and extracting the standard MCC fingerprint features is the same as the method for fusing the machine learning fingerprint features to be authenticated and the MCC fingerprint features to be authenticated in step S102, and will not be repeated here.

[0067] It should be noted that the execution order of the remote server extracting the fingerprint features to be authenticated from the fingerprint image to be authenticated (i.e., step S102) and extracting the standard fingerprint features from the standard fingerprint image is not important.

[0068] After the remote server extracts the fingerprint features to be authenticated and the standard fingerprint features, it can authenticate the user's identity based on these two features. Specifically, the similarity between the fingerprint features to be authenticated and the standard fingerprint features can be calculated. If the similarity is higher than a set threshold, authentication is successful; otherwise, authentication fails. The similarity can be represented by Euclidean distance or cosine distance.

[0069] Furthermore, the aforementioned fingerprint feature extraction model can be pre-trained using a contrastive learning method. Specifically, sample fingerprint images and standard fingerprint images of the sample users can be acquired. The sample fingerprint images are acquired using a non-contact acquisition method, while the standard fingerprint images are acquired using a contact acquisition method. Using the fingerprint feature extraction model to be trained, sample machine learning fingerprint features are extracted from the sample fingerprint images, and sample standard machine learning fingerprint features are extracted from the sample standard fingerprint images. Additionally, sample MCC fingerprint features are extracted from the sample fingerprint images, and sample standard MCC fingerprint features are extracted from the sample standard fingerprint images. The sample machine learning fingerprint features and sample MCC fingerprint features are fused to obtain sample fingerprint features. The sample standard machine learning fingerprint features and sample standard MCC fingerprint features are fused to obtain sample standard fingerprint features. A loss value is calculated based on the sample fingerprint features, the sample standard fingerprint features, and a preset similarity loss function. The fingerprint feature extraction model to be trained is then trained based on the loss value. Among them, the similarity between the sample fingerprint features and the sample standard fingerprint features is negatively correlated with the loss value of the loss function. That is, the higher the similarity between the sample fingerprint features and the sample standard fingerprint features, the lower the loss value, and vice versa.

[0070] The device used to train the fingerprint feature extraction model can be either the remote server itself or other devices. After training the fingerprint feature extraction model using the above method, the model can be deployed on the remote server.

[0071] Using the above method, mobile terminals no longer authenticate users based on locally stored fingerprints collected using contactless methods. Instead, they acquire the fingerprint image to be authenticated using a contactless method, and then authenticate the user based on a standard fingerprint image acquired by an authoritative institution using a contactless method. This breaks down the barriers between fingerprints acquired using two different methods and improves the authentication credibility of mobile terminals. When migrating services with strict authentication requirements online, mobile terminals can acquire the user's fingerprint image to be authenticated using a contactless method and send it to a remote server. The remote server then extracts the fingerprint features to be authenticated from the fingerprint image and obtains the user's standard fingerprint image acquired by a contactless method from an authoritative institution. It extracts the standard fingerprint features from the standard fingerprint image and finally authenticates the user based on the fingerprint features to be authenticated and the standard fingerprint features. The authentication result is then transmitted to the service providers performing these services with strict authentication requirements, allowing them to continue with subsequent operations based on the authentication result.

[0072] In the embodiments of this specification, when the mobile terminal collects a user's fingerprint image to be authenticated using a contactless collection method, it may first display a prompt message to the user. This prompt message is used to indicate to the user the finger to be collected and the collection method, such as... Figure 2 As shown. In Figure 2 The message "Please place your right index finger inside the circle to take the photo" is a prompt indicating that the finger to be photographed is the right index finger, and the method is to place the front of the right index finger inside the circle to take a picture. Of course, those skilled in the art should understand that which finger needs to be photographed needs to be determined based on the finger corresponding to the standard fingerprint image pre-stored by an authoritative institution.

[0073] In addition, after acquiring the fingerprint image to be authenticated, the mobile terminal can first perform quality checks and liveness detection on the fingerprint image. Only after both checks pass can the image be sent to a remote server for fingerprint feature extraction. Alternatively, the mobile terminal can directly send the fingerprint image to the remote server after acquisition. The remote server will then perform quality checks and liveness detection on the fingerprint image, and only after both checks pass can the fingerprint features be extracted. Regardless of which device performs the quality checks and liveness detection, the methods for quality checks and liveness detection can be implemented using the following methods:

[0074] Quality detection. Environmental features are acquired when the fingerprint image to be authenticated is collected. These environmental features include at least one of lighting features and background noise features. Based on the environmental features, an image quality characterization value for the fingerprint image to be authenticated is determined. If the image quality characterization value meets a preset condition, the quality detection is deemed successful, and the fingerprint features to be authenticated can be extracted from the fingerprint image. Meeting the preset condition may mean that the image quality characterization value is higher than a preset image quality threshold.

[0075] The quality detection described in this specification may include the texture sharpness and finger deflection of the fingerprint image to be authenticated, which can be achieved through a machine learning model. Specifically, the environmental features and the fingerprint image to be authenticated can be input into a pre-trained image quality assessment model, so that the image quality assessment model determines the texture sharpness quality and deflection of the fingerprint image to be authenticated, and obtains the image quality characterization value of the fingerprint image to be authenticated based on the texture sharpness quality and the deflection.

[0076] Liveness detection. Perform liveness detection on the fingerprint image to be authenticated; if the liveness detection passes, extract the fingerprint features to be authenticated from the fingerprint image.

[0077] Liveness detection can also be achieved using machine learning models. Specifically, the fingerprint image to be authenticated and environmental features can be input into a pre-trained liveness detection model to perform liveness detection on the fingerprint image. This is to prevent unauthorized users from using mobile terminals to collect photos of legitimate users' fingers to obtain the fingerprint image to be authenticated, thus preventing them from impersonating legitimate users to pass identity authentication.

[0078] When training a liveness detection model, it can learn the difference between the light reflected from human skin and the light reflected from a printed photograph, as well as the texture characteristics of human skin. The model can also be trained using custom-generated positive and negative samples. Specifically, fingerprint images of sample users can be collected as positive samples, and photographic and printed images of those fingerprints can be generated as negative samples. The liveness detection model is then trained based on these positive and negative samples.

[0079] Those skilled in the art should understand that the above method is only an example of implementing an identity authentication method through a system consisting of a mobile terminal and a remote server. The above identity authentication method can also be implemented by a mobile terminal or a remote server alone, which are equivalent substitution methods and are also within the scope of this application.

[0080] The above is an example of an identity authentication method provided in this specification. Based on the same idea, this specification also provides corresponding systems, devices, storage media, and electronic devices.

[0081] Figure 3 This is a schematic diagram of an identity authentication system architecture provided in an embodiment of this specification. The system includes: a terminal 301 and a remote server 302.

[0082] The terminal 301 collects the user's fingerprint image to be authenticated using a non-contact acquisition method and sends the fingerprint image to be authenticated to the remote server 302.

[0083] The remote server 302 extracts fingerprint features to be authenticated from the fingerprint image to be authenticated; and authenticates the user's identity based on the fingerprint features to be authenticated and the standard fingerprint features extracted from a pre-saved standard fingerprint image, wherein the standard fingerprint image is a fingerprint image pre-saved in an authoritative institution and collected by a contact acquisition method.

[0084] Figure 4 This is a schematic diagram of an identity authentication device provided in an embodiment of this specification. The device includes:

[0085] The acquisition module 401 acquires the user's fingerprint image to be authenticated using a non-contact acquisition method;

[0086] Extraction module 402 extracts fingerprint features to be authenticated from the fingerprint image to be authenticated;

[0087] The authentication module 403 authenticates the user's identity based on the fingerprint features to be authenticated and the standard fingerprint features extracted from a pre-saved standard fingerprint image. The standard fingerprint image is a fingerprint image pre-saved in an authoritative institution and acquired through a contact acquisition method.

[0088] Optionally, the acquisition module 401 is specifically used to acquire the user's fingerprint image to be authenticated through a camera.

[0089] Optionally, the extraction module 402 is specifically used to: acquire environmental features when the fingerprint image to be authenticated is acquired, the environmental features including at least one of light features and background noise features; determine the image quality characterization value of the fingerprint image to be authenticated based on the environmental features; and extract the fingerprint features to be authenticated from the fingerprint image to be authenticated if the image quality characterization value meets a preset condition.

[0090] Optionally, the extraction module 402 is specifically used to input the environmental features and the fingerprint image to be authenticated into a pre-trained image quality evaluation model, so that the image quality evaluation model determines the texture sharpness quality and the degree of deflection of the fingerprint image to be authenticated, and obtains the image quality characterization value of the fingerprint image to be authenticated based on the texture sharpness quality and the degree of deflection.

[0091] Optionally, the extraction module 402 is specifically used to perform liveness detection on the fingerprint image to be authenticated; if the liveness detection is successful, the fingerprint features to be authenticated are extracted from the fingerprint image to be authenticated.

[0092] Optionally, the extraction module 402 is specifically used to: input the fingerprint image to be authenticated into a pre-trained fingerprint feature extraction model, so as to extract the machine learning fingerprint features to be authenticated from the fingerprint image to be authenticated through the fingerprint feature extraction model; and extract the MCC fingerprint features to be authenticated from the fingerprint image to be authenticated; and fuse the machine learning fingerprint features to be authenticated and the MCC fingerprint features to be authenticated as the fingerprint features to be authenticated.

[0093] The authentication module 403 is specifically used to input the standard fingerprint image into a pre-trained fingerprint feature extraction model to extract standard machine learning fingerprint features from the standard fingerprint image through the fingerprint feature extraction model; and to extract standard MCC fingerprint features from the standard fingerprint image; and to fuse the standard machine learning fingerprint features and the standard MCC fingerprint features as standard fingerprint features.

[0094] Optionally, the device further includes:

[0095] Training module 404 is used to acquire sample fingerprint images of sample users and sample standard fingerprint images of the sample users. The sample fingerprint images are acquired using a non-contact acquisition method, and the sample standard fingerprint images are acquired using a contact acquisition method. Using a fingerprint feature extraction model to be trained, it extracts sample machine learning fingerprint features from the sample fingerprint images and sample standard machine learning fingerprint features from the sample standard fingerprint images; it also extracts sample MCC fingerprint features from the sample fingerprint images and sample standard MCC fingerprint features from the sample standard fingerprint images; it fuses the sample machine learning fingerprint features and sample MCC fingerprint features to obtain sample fingerprint features; it fuses the sample standard machine learning fingerprint features and sample standard MCC fingerprint features to obtain sample standard fingerprint features; it calculates a loss value based on the sample fingerprint features, the sample standard fingerprint features, and a preset similarity loss function; and it trains the fingerprint feature extraction model to be trained based on the loss value.

[0096] This specification also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can be used to perform the above-described actions. Figure 1 The provided identity authentication method.

[0097] based on Figure 1 The following is based on Figure 1 The risk control method shown in this specification, in addition to the embodiments, also provides Figure 5 The diagram shows the structure of the electronic device. Figure 5 At the hardware level, this electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 The method of identity authentication described above.

[0098] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0099] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0100] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0101] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0102] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0103] Those skilled in the art will understand that embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0108] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0109] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0110] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0111] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0113] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0114] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for identity authentication, the method comprising: The user's fingerprint image to be authenticated is collected using a non-contact acquisition method; Extract the fingerprint features to be authenticated from the fingerprint image to be authenticated; The user is authenticated based on the fingerprint features to be authenticated and the standard fingerprint features extracted from a pre-saved standard fingerprint image, wherein the standard fingerprint image is a fingerprint image pre-saved in an authoritative institution and collected by a contact acquisition method. Extracting fingerprint features from the fingerprint image to be authenticated specifically includes: The fingerprint image to be authenticated is input into a pre-trained fingerprint feature extraction model to extract the machine learning fingerprint features to be authenticated from the fingerprint image to be authenticated; and the MCC fingerprint features to be authenticated are extracted from the fingerprint image to be authenticated. The machine learning fingerprint features to be authenticated and the MCC fingerprint features to be authenticated are combined to form the fingerprint features to be authenticated. Extracting standard fingerprint features from pre-saved standard fingerprint images, specifically including: The standard fingerprint image is input into a pre-trained fingerprint feature extraction model to extract standard machine learning fingerprint features from the standard fingerprint image; and standard MCC fingerprint features are extracted from the standard fingerprint image. The standard machine learning fingerprint features and the standard MCC fingerprint features are combined to form the standard fingerprint features.

2. The method as described in claim 1, wherein the user's fingerprint image to be authenticated is acquired through a non-contact acquisition method, specifically includes: The system uses a camera to capture the user's fingerprint image to be authenticated.

3. The method as described in claim 1, wherein extracting the fingerprint features to be authenticated from the fingerprint image to be authenticated specifically includes: The environmental features acquired during the collection of the fingerprint image to be authenticated include at least one of light features and background noise features. Based on the environmental characteristics, determine the image quality characterization value of the fingerprint image to be authenticated; If the image quality characterization value meets the preset conditions, then the fingerprint features to be authenticated are extracted from the fingerprint image to be authenticated.

4. The method as described in claim 3, wherein determining the image quality characterization value of the fingerprint image to be authenticated based on the environmental characteristics specifically includes: The environmental features and the fingerprint image to be authenticated are input into a pre-trained image quality evaluation model so that the image quality evaluation model can determine the texture sharpness quality and the degree of deflection of the fingerprint image to be authenticated, and obtain the image quality characterization value of the fingerprint image to be authenticated based on the texture sharpness quality and the degree of deflection.

5. The method as described in claim 1, wherein extracting the fingerprint features to be authenticated from the fingerprint image to be authenticated specifically includes: Perform liveness detection on the fingerprint image to be authenticated; If the liveness detection passes, the fingerprint features to be authenticated are extracted from the fingerprint image to be authenticated.

6. The method as described in claim 1, wherein the fingerprint feature extraction model is pre-trained, specifically comprising: The sample fingerprint image and the sample standard fingerprint image of the sample user are acquired. The sample fingerprint image is acquired by a non-contact acquisition method, and the sample standard fingerprint image is acquired by a contact acquisition method. Using the fingerprint feature extraction model to be trained, sample machine learning fingerprint features are extracted from the sample fingerprint image, and sample standard machine learning fingerprint features are extracted from the sample standard fingerprint image; In addition, sample MCC fingerprint features are extracted from the sample fingerprint image, and sample standard MCC fingerprint features are extracted from the sample standard fingerprint image; By fusing the sample machine learning fingerprint features and the sample MCC fingerprint features, the sample fingerprint features are obtained; By fusing the sample standard machine learning fingerprint features and the sample standard MCC fingerprint features, the sample standard fingerprint features are obtained; The loss value is calculated based on the sample fingerprint features, the sample standard fingerprint features, and the preset similarity loss function; The fingerprint feature extraction model to be trained is trained based on the loss value.

7. An identity authentication system, the system comprising: Terminal, remote server; The terminal acquires the user's fingerprint image to be authenticated using a non-contact acquisition method and sends the fingerprint image to the remote server. The remote server extracts the fingerprint features to be authenticated from the fingerprint image to be authenticated. The user is authenticated based on the fingerprint features to be authenticated and the standard fingerprint features extracted from a pre-saved standard fingerprint image, wherein the standard fingerprint image is a fingerprint image pre-saved in an authoritative institution and collected by a contact acquisition method. The remote server is specifically used to input the fingerprint image to be authenticated into a pre-trained fingerprint feature extraction model, so as to extract the machine learning fingerprint features to be authenticated from the fingerprint image to be authenticated through the fingerprint feature extraction model; and to extract the MCC fingerprint features to be authenticated from the fingerprint image to be authenticated; and to fuse the machine learning fingerprint features to be authenticated and the MCC fingerprint features to be authenticated as the fingerprint features to be authenticated. The remote server is specifically used to input the standard fingerprint image into a pre-trained fingerprint feature extraction model to extract standard machine learning fingerprint features from the standard fingerprint image through the fingerprint feature extraction model; and to extract standard MCC fingerprint features from the standard fingerprint image; and to fuse the standard machine learning fingerprint features and the standard MCC fingerprint features as standard fingerprint features.

8. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1-7.

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