Identify recognition method and apparatus, device, medium, and program product

Abstract

The present application provides an identity recognition method and apparatus, a device, a medium, and a program product, which may relate to Internet technology. The method comprises: acquiring a current biological feature of a first object; recognizing identity information of the first object on the basis of the current biological feature and respective first biological features of N objects, wherein N is a positive integer; and if the identity information of the first object has failed to be recognized on the basis of the current biological feature of the first object and the respective first biological features of the N objects, recognizing the identity information of the first object on the basis of the current biological feature of the first object and respective second biological features of the N objects. Therefore, the object experience can be improved.

Description

Identification methods, devices, equipment, media and program products

[0001] This application claims priority to Chinese Patent Application No. 202410959766.6, filed on July 16, 2024, entitled "Identification Method, Apparatus, Device, Medium and Program Product", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of Internet technology, and in particular to an identity recognition method, apparatus, device, medium, and program product. Background Technology

[0003] With the continuous development of Internet technology, object identification has been applied to more and more business scenarios, such as payment scenarios and membership points scenarios, thereby making life more convenient for individuals.

[0004] Currently, servers used for identity verification can store a biometric feature for each object, such as palm print, facial features, or iris features. A terminal device can capture an image of an object's current biometric features and send it to the server. The server extracts features from the image to obtain the object's current biometric features. Further, the server can identify the object based on these current biometric features and the biometric features of N objects; this identification method can be called 1vN identity verification. If the 1vN identification method successfully identifies the object, the server can use this information for payments, membership points, etc. If the 1vN identification method fails, the server needs to guide the object to enter their mobile phone number on the terminal device to determine the object's stored biometric features corresponding to that number, and then identify the object based on both the object's current and stored biometric features; this identification method can be called 1v1 identity verification. Typically, 1v1 identity verification has slightly lower requirements than 1vN identity verification. The identity verification stage achieved through 1v1 identity verification is also known as the 1v1 verification stage or the 1v1 mobile phone number verification stage.

[0005] However, in some complex scenarios, factors such as object posture and environmental conditions can cause problems. For example, in palmprint recognition, the angle of the palm print can make features in some areas of the palm less distinct, or environmental changes, such as dirt on the palm, can also lead to the loss or obscuration of features in other areas. This can result in the failure of 1vN identity verification, often necessitating a 1v1 phone number verification stage. This increases the time required for identity verification and necessitates user interface (UI) interaction, ultimately leading to a poor user experience. Summary of the Invention

[0006] This application provides an identity recognition method, apparatus, device, medium, and program product, which can improve the user experience.

[0007] In a first aspect, embodiments of this application provide an identity recognition method, comprising: obtaining the current biometric features of a first object; recognizing the identity information of the first object based on the current biometric features and the first biometric features of N objects respectively; wherein, N is a positive integer; if the recognition of the identity information of the first object based on the current biometric features and the first biometric features of N objects fails, then recognizing the identity information of the first object based on the current biometric features and the second biometric features of N objects respectively.

[0008] Secondly, embodiments of this application provide an identity recognition device, including: an acquisition module and an identity recognition module, wherein the acquisition module is used to acquire the current biometric features of a first object; the identity recognition module is used to: recognize the identity information of the first object based on the current biometric features and the first biometric features of N objects respectively; wherein N is a positive integer; if the recognition of the identity information of the first object based on the current biometric features and the first biometric features of N objects fails, then the identity information of the first object is recognized based on the current biometric features and the second biometric features of N objects respectively.

[0009] In some implementations, the identity recognition module is specifically used to: identify the identity information of the first object based on the current biometrics and the second biometrics of each of the N objects.

[0010] In some implementation methods, the identity recognition module is specifically used to: detect a second object among N objects that is suspected to be the first object; if there is a second object among the N objects and the second object has a second biometric feature, then the identity information of the first object is identified based on the current biometric feature and the second biometric feature of the second object.

[0011] In some implementations, the identity recognition module is specifically used to: reduce the first similarity threshold used to identify the identity information of the first object based on the current biometric feature and the first biometric features of each of the N objects, to obtain a second similarity threshold; calculate the similarity between the current biometric feature and the first biometric features of each of the N objects; and identify the object among the N objects whose similarity is greater than the second similarity threshold as the second object.

[0012] In some implementations, the identity recognition module is specifically used to: reduce the first scoring threshold used to identify the identity information of the first object based on the current biometric feature and the first biometric features of each of the N objects, and obtain a second scoring threshold; calculate the similarity between the current biometric feature and the first biometric features of each of the N objects, and determine the similarity score corresponding to each similarity; and identify the object among the N objects whose similarity score is greater than the second scoring threshold as the second object.

[0013] In some implementations, the identity recognition module is specifically used to: if the second object has multiple second biometric features, calculate the similarity between the current biometric feature and each of the second biometric features of the second object; and identify the identity information of the first object based on the similarity between the current biometric feature and each of the second biometric features of the second object.

[0014] In some implementation methods, the identity recognition module is specifically used to: if the second object has multiple second biometric features, calculate the similarity between the current biometric feature and the multiple second biometric features of the second object in descending order of quality of the biometric feature images corresponding to each of the multiple second biometric features; if the similarity between the current biometric feature and the target second biometric feature of the second object is greater than a third similarity threshold, use the identity information of the second object as the identity information of the first object, and stop calculating the similarity between the current biometric feature and other second biometric features of the second object.

[0015] In some implementations, the identity recognition module is also used to: if there is no second object among the N objects, then recognize the identity information of the first object based on the terminal identifier of the first object.

[0016] In some implementations, the identity recognition module is also used to: if the second object does not have a second biometric feature, then identify the identity information of the first object based on the terminal identifier of the first object.

[0017] In some implementation methods, the identity recognition module is specifically used to: detect the activation status of the biometric recognition-related functions of the first object based on the terminal identifier of the first object; if the first object has activated the biometric recognition-related functions, then identify the identity information of the first object based on the current biometrics and the first biometrics of the first object.

[0018] In some implementations, the device further includes a storage model for: if the identity information of the first object is successfully identified based on the current biometrics and the first biometrics of the first object, then the current biometrics is used as the second biometrics of the first object, and the second biometrics of the first object is stored.

[0019] In some implementations, the device further includes a determining model for determining the quality score of the current biometric image corresponding to the current biometric before the storage model uses the current biometric as the second biometric of the first object and stores the second biometric of the first object; correspondingly, the storage model is specifically used to: if the quality score of the current biometric image is greater than a preset score, then use the current biometric as the second biometric of the first object and store the second biometric of the first object.

[0020] In some implementations, the model is specifically used to: input the current biometric image into a neural network model to obtain a quality score for the current biometric image.

[0021] In some implementations, the device also includes a training model, and the acquisition module is further used to acquire training samples of the neural network model; the training model is used to train the neural network model using the training samples of the neural network model.

[0022] In some implementations, the acquisition module is specifically used to: acquire M biometric images of the third object; where M is an integer greater than 1; select the target biometric image with the highest quality among the M biometric images; calculate the similarity between the other M-1 biometric images (excluding the target biometric image) and the target biometric image; and form M-1 training samples for the neural network model by combining the similarity between the other M-1 biometric images and the target biometric image.

[0023] In some implementations, the device also includes a triggering model, wherein if the first object has not enabled biometric identification-related functions, the triggering model is used to trigger the first object to enable biometric identification-related functions.

[0024] In some possible implementations, if the first object has not enabled biometric recognition functions, the storage model is also used to use the current biometrics as the first biometrics of the first object and store the first biometrics of the first object.

[0025] In some implementations, the acquisition module is specifically used to: acquire the current biometric image of the first object; and extract features from the current biometric image to obtain the current biometric features of the first object.

[0026] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to perform the methods as described in the first aspect or its various implementations.

[0027] Fourthly, embodiments of this application provide a computer-readable storage medium for storing a computer program that causes a computer to perform the methods described in the first aspect or its various implementations.

[0028] Fifthly, embodiments of this application provide a computer program product including computer program instructions that cause a computer to perform the methods as described in the first aspect or its various implementations.

[0029] Sixthly, embodiments of this application provide a computer program that causes a computer to perform the methods as described in the first aspect or its various implementations.

[0030] The technical solution provided in this application enables the system to bypass the 1v1 phone number verification stage after a 1vN identification failure. Instead, it adds a second biometric verification step. The 1v1 phone number verification stage is only entered when the second biometric verification fails. When the second biometric verification is successful, the 1v1 phone number verification stage is not required. This shortens the identification time and eliminates the need for UI interaction by the user, ultimately improving the user experience. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 is a schematic diagram of a system architecture according to an embodiment of this application;

[0033] Figure 2 is a flowchart of an identity recognition method provided in an embodiment of this application;

[0034] Figure 3 is a schematic diagram of the sixth neural network model provided in the embodiment of this application;

[0035] Figure 4 is a schematic diagram of biometric image selection provided in an embodiment of this application;

[0036] Figure 5 is a schematic diagram of the generation of training samples provided in the embodiments of this application;

[0037] Figure 6 is a schematic diagram of preprocessing and model training provided in an embodiment of this application;

[0038] Figure 7 is a flowchart of another identity recognition method provided in an embodiment of this application;

[0039] Figure 8 is a schematic diagram of an identity recognition device 800 provided in an embodiment of this application;

[0040] Figure 9 is a schematic block diagram of an electronic device 900 provided in an embodiment of this application. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0043] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0044] The embodiments of this application may relate to mobile payment in Internet technology, such as palm payment.

[0045] Internet technology refers to an information technology developed on the basis of computer technology. It connects individual computers or networks to form a specific network system and realizes the transmission and sharing of information.

[0046] Mobile payment refers to the use of mobile devices such as smartphones to make electronic payments. It effectively connects the internet, terminal devices, and financial institutions, forming a new payment system. Mobile payment has pioneered new payment methods and made electronic money more widespread.

[0047] Palm payment is a biometric payment method that primarily utilizes palmprint recognition technology. Palmprint recognition technology scans the texture features of the palm, extracts unique palmprint characteristics, and compares them with pre-stored palmprint features to verify the recipient's identity and complete the payment. The advantages of palm payment technology lie in its high security and accuracy. Each person's palmprint is unique, making it difficult to copy or forge, greatly enhancing payment security. Simultaneously, palmprint recognition technology boasts high accuracy, accurately identifying the recipient and reducing the risk of misjudgments and incorrect payments. Furthermore, palm payment technology is convenient. The recipient does not need to carry any physical cards or mobile phones; simply placing their palm on the payment device completes the payment, greatly simplifying the process and improving efficiency.

[0048] Palm payment technology can also use palmprint and palm vein recognition technology, which can adapt to complex lighting environments, improve payment efficiency, and simplify object handling.

[0049] In this embodiment of the application, mobile payment can be realized after object identification is performed through terminal devices, such as a swipe device and a payment backend server.

[0050] It should be understood that the embodiments of this application may not be limited to mobile payment in Internet technology, such as membership points, or renting power banks for free by swiping one's palm in Internet technology.

[0051] Before introducing the technical solution of this application, the relevant knowledge of this application will be explained below:

[0052] I. Biometrics refers to the unique, measurable physical or behavioral characteristics associated with an organism that can be used to identify or verify the identity of an individual. Common biometrics include: fingerprints, palm prints, iris scans, facial features, etc.

[0053] II. The 1vN identity recognition method refers to the method of identity recognition based on a current biometric feature and N stored biometric features. Specifically, the similarity between the current biometric feature and each of the N stored biometric features can be calculated. The biometric feature with a similarity greater than the similarity threshold among the N stored biometric features is selected, and the identity information of the object corresponding to the biometric feature is used as the identity information of the object corresponding to the current biometric feature.

[0054] 3. The 1v1 identity recognition method refers to the method of identity recognition based on a currently acquired biometric feature and a stored biometric feature. Specifically, the similarity between the current biometric feature and the stored biometric feature can be calculated. If the similarity is greater than the similarity threshold, the identity information of the object corresponding to the current biometric feature is used as the identity information of the object corresponding to the current biometric feature.

[0055] IV. Palm Scan Payment: A payment method that uses the user's palm print for payment authentication.

[0056] V. Instant messaging clients are communication tools based on internet technology, allowing users to communicate in real time via text, voice, video, and other methods. Common instant messaging clients include WeChat, QQ, and WeChat Work, but are not limited to these. These communication tools typically offer features such as instant messaging, voice calls, video calls, and file transfer, enabling users to communicate and collaborate conveniently.

[0057] The technical problems to be solved, the inventive concept and the system architecture of the embodiments of this application will be described below:

[0058] As mentioned above, in some complex scenarios, factors such as object posture and environmental conditions can cause issues. For example, in palmprint recognition, the angle of the palm print can make features in some areas of the palm less distinct, or environmental changes, such as dirt on the palm, can also lead to the loss or obscuration of features in other areas. This can result in the failure of 1vN identity verification, often necessitating a 1v1 phone number verification stage. This leads to longer identity verification times and requires UI interaction from the user, ultimately resulting in a poor user experience.

[0059] In related technologies, for situations where identity recognition is affected by object pose, environment, etc., the main approach is to train the neural network model used in the 1vN identity recognition stage by augmenting the training data. This improves the model's generalization ability and robustness. Specifically, more training data on object pose and environment is needed to train the neural network model, so that even if the object's pose and environment change, it can still be identified. However, in reality, on the one hand, this training data may not cover all pose and environment changes, leading to the possibility of failure in the 1vN identity recognition method; on the other hand, augmenting the training data increases the training cost.

[0060] To address the aforementioned technical issues, this application proposes that a backend server can store multiple biometric features of an object, such as biometric features of the object in different postures or environments. This allows the system to bypass the 1v1 phone number verification stage after a 1vN identification failure, instead adding a second biometric verification step. Only if this second biometric verification fails will the system proceed to the 1v1 phone number verification stage. If the second biometric verification is successful, the 1v1 phone number verification stage can be skipped, thus shortening the identification time and eliminating the need for UI interaction by the object, ultimately improving the user experience.

[0061] In some possible implementations, the system architecture of this application embodiment is shown in Figure 1.

[0062] Figure 1 is a schematic diagram of a system architecture according to an embodiment of this application, showing a terminal device 110 and a backend server 120. The terminal device 110 and the backend server 120 can be directly or indirectly connected via wired or wireless communication, which is not limited herein.

[0063] The terminal device 110 may have an application (APP) installed to implement biometric recognition-related functions. These functions may include, but are not limited to, payment, membership points, and free power bank rental based on identity information obtained through biometric recognition.

[0064] In some feasible ways, biometric identification-related functions can be applied to payment applications, membership applications, power bank rental applications, etc., but are not limited to these.

[0065] In some implementations, the payment application can be any application that supports payment functionality; for example, the payment application can be an instant messaging client with payment capabilities.

[0066] In some implementations, a camera module is installed on the terminal device 110 for acquiring biometric images. The camera module may include an image acquisition device and a communication device. The image acquisition device may be, for example, a 3D camera, in which hardware and software related to image quality detection may be added, including a depth camera and an infrared camera. The communication device may be at least one of a wireless communication device (e.g., Bluetooth) and a wired communication device.

[0067] In some possible implementations, the terminal device 110 may include, but is not limited to: mobile phones, computers, smart voice interaction devices, smartwatches, virtual reality (VR), augmented reality (AR), smart home appliances, vehicle terminals, aircraft, point-of-sale (POS) terminals, cash registers, self-checkout systems, and QR code (payment code) scanning boxes.

[0068] In some implementation methods, the backend server 120 can be the backend server corresponding to an application that implements biometric identification-related functions, such as a payment backend server. The backend server 120 can also be the backend server corresponding to a mini-program that implements biometric identification-related functions. For example, when the application that implements biometric identification-related functions is an instant messaging client, the backend server can be the backend server corresponding to a membership points mini-program or a power bank rental mini-program.

[0069] In some implementations, the backend server 120 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0070] The backend server 120 can obtain the current biometric features of the first object; based on the current biometric features and the first biometric features of each of the N objects, the identity information of the first object is identified; where N is a positive integer; if the identification of the identity information of the first object based on the current biometric features and the first biometric features of each of the N objects fails, the identity information of the first object is identified based on the current biometric features and the second biometric features of each of the N objects.

[0071] Figure 1 illustrates a terminal device. Optionally, the system architecture may include multiple terminal devices, all of which are connected to the backend server 120. In summary, the system architecture used in the embodiments of this application is not limited to the system architecture shown in Figure 1.

[0072] Before introducing the embodiments of this application, it should be noted that the information involved in the embodiments of this application, including biometrics, object identity information, biometric images, etc., are all authorized by the relevant objects or fully authorized by all parties, and the collection, use and processing of relevant information comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0073] The embodiments of this application will be described in detail below:

[0074] Figure 2 is a flowchart of an identity recognition method provided in an embodiment of this application. As shown in Figure 2, the method can be a backend server corresponding to an application that implements biometric recognition-related functions. For example, the backend server can be a payment backend server, a membership points mini-program, or a power bank rental mini-program, but it is not limited to these. As shown in Figure 2, the method can include:

[0075] S210: Obtain the current biometric characteristics of the first object;

[0076] One possible approach is for the backend server to obtain the current biometric image of the first object; then, feature extraction is performed on the current biometric image to obtain the current biometric features of the first object.

[0077] In some implementations, the terminal device can capture a current biometric image of the first object using a camera module and send the image to a backend server. For example, a palm-swiping device can capture a current biometric image of the first object using its camera module and send the image to a backend server.

[0078] In some implementations, the backend server can input the currently acquired biometric image into a first neural network model to obtain the current biometric features of the first object.

[0079] In some implementations, the backend server can preprocess the current biometric image before inputting it into the first neural network model. Accordingly, the backend server can input the preprocessed current biometric image into the first neural network model to obtain the current biometric features of the first object.

[0080] In some possible implementations, the preprocessing includes at least one of the following, but is not limited to: image denoising and image enhancement.

[0081] In some possible implementations, the first neural network model can be a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory (LSTM), a gated recurrent unit (GRU), a bidirectional long short-term memory network (BiLSTM), a bidirectional gated recurrent unit (BiGRU), a transformer, or a bidirectional encoder representation from transformers (BERT) model, but is not limited to these.

[0082] In the second possible implementation, the terminal device can acquire the current biometric image of the first object through the camera module; and extract features from the current biometric image to obtain the current biometric features of the first object; the backend server receives the current biometric features of the first object sent by the terminal device.

[0083] It should be understood that the difference between feasible method two and feasible method one is that in feasible method two, the terminal device extracts features from the currently acquired biometric image, while in feasible method one, the backend server extracts features from the currently acquired biometric image. Therefore, the feature extraction method used by the terminal device for the currently acquired biometric image can refer to the feature extraction method used by the backend server for the currently acquired biometric image, and will not be elaborated further.

[0084] In some possible implementations, the current biometrics of the first object can be fingerprint features, palm print features, iris features, facial features, etc., but are not limited to these.

[0085] S220: Based on the current biometric features and the first biometric features of each of the N objects, identify the identity information of the first object; where N is a positive integer;

[0086] It should be understood that the backend server has stored the first biometric features of each of the N objects. For each of the N objects, the first biometric feature refers to the biometric feature used in the 1vN identity verification and 1v1 mobile phone number verification processes. In other words, the first biometric feature is the biometric feature that has been stored for that object in the existing technology. Each object has one first biometric feature. The first biometric feature is also referred to as the master biometric feature or main feature.

[0087] For example, assuming that the biometric feature in this application embodiment refers to palm print feature, the backend server can store one palm print feature for each of N objects for use in subsequent 1vN identity recognition and 1v1 mobile phone number verification processes.

[0088] It should be understood that for each of the N objects, the backend server can obtain multiple biometric features of that object, but only stores the best quality biometric feature of that object as the first biometric feature of that object.

[0089] In some feasible implementations, for each of the N objects, the backend server can input the current biometrics of the first object and the first biometrics of the object into a second neural network model to obtain the similarity between the current biometrics of the first object and the first biometrics of the object, and identify the identity information of the first object based on the similarity and a first similarity threshold.

[0090] It should be understood that for each of the N objects, the backend server stores not only the object's first biometric feature, but also the object's identity information, where the object's identity information corresponds to the object's first biometric feature.

[0091] In some possible implementations, for each of the N objects, if the similarity between the current biometrics of the first object and the first biometrics of the object is greater than a first similarity threshold, then the backend server uses the identity information of the object corresponding to the first biometrics of the object as the identity information of the first object.

[0092] In some possible implementations, for each of the N objects, if the similarity between the current biometrics of the first object and the first biometrics of the object is greater than or equal to a first similarity threshold, then the backend server uses the identity information of the object corresponding to the first biometrics of the object as the identity information of the first object.

[0093] In some implementations, the second neural network model can be a CNN, RNN, LSTM, GRU, BiLSTM, BiGRU, Transformer, BERT model, but is not limited to these.

[0094] In some feasible implementations, for each of the N objects, the backend server can input the current biometrics of the first object and the first biometrics of the object into a third neural network model to obtain a similarity score between the current biometrics of the first object and the first biometrics of the object, and identify the identity information of the first object based on the similarity score and the score threshold.

[0095] In some implementations, for each of the N objects, the backend server can use a third neural network model to calculate the similarity between the current biometrics of the first object and the first biometrics of the object, and determine the similarity score between the current biometrics of the first object and the first biometrics of the object based on the similarity.

[0096] In some implementation methods, the similarity interval [0,1] can be divided into multiple sub-intervals, each sub-interval corresponding to a similarity score. Based on this, the backend server uses a third neural network model to determine the similarity sub-intervals corresponding to the similarity between the current biometric feature of the first object and the first biometric feature of the object, and determines the similarity score corresponding to the sub-interval as the similarity score between the current biometric feature of the first object and the first biometric feature of the object.

[0097] For example, suppose the similarity [0,1] is divided into 10 sub-intervals, namely [0,0.1], (0.1,0.2], (0.2,0.3], (0.3,0.4], (0.4,0.5], (0.5,0.6], (0.6,0.7], (0.7,0.8], (0.8,0.9], (0.9,1], and their corresponding similarity scores are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.

[0098] It should be understood that for each of the N objects, the backend server stores not only the object's first biometric feature, but also the object's identity information, where the object's identity information corresponds to the object's first biometric feature.

[0099] In some possible implementations, for each of the N objects, if the similarity score between the current biometrics of the first object and the first biometrics of the object is greater than a first score threshold, then the backend server uses the identity information of the object corresponding to the first biometrics of the object as the identity information of the first object.

[0100] In some possible implementations, for each of the N objects, if the similarity score between the current biometrics of the first object and the first biometrics of the object is greater than or equal to a first score threshold, then the backend server uses the identity information of the object corresponding to the first biometrics of the object as the identity information of the first object.

[0101] In some implementations, the third neural network model can be a CNN, RNN, LSTM, GRU, BiLSTM, BiGRU, Transformer, BERT model, but is not limited to these.

[0102] It should be understood that the identity information of the first party is used to achieve subsequent payment tasks, membership points tasks, or tasks such as renting power banks for free.

[0103] In some possible implementations, the identity information of the first object includes at least one of the following, but is not limited to: basic information, contact information, account information, and address information.

[0104] In some possible implementations, the basic information of the first object includes at least one of the following, but is not limited to: the first object's name, gender, age, and ID number.

[0105] In some possible implementations, the contact information of the first object includes at least one of the following, but is not limited to: the first object's phone number and email address.

[0106] In some possible implementations, the account information of the first object includes at least one of the following, but is not limited to: third-party payment account, bank card information, instant messaging client payment account.

[0107] It should be understood that S220 is the 1vN identity verification process.

[0108] S230: If the identification of the identity information of the first object fails based on the current biometric feature and the first biometric feature of each of the N objects, then the identification of the identity information of the first object is performed based on the current biometric feature and the second biometric feature of each of the N objects.

[0109] It should be understood that the backend server has stored the second biometric features of each of the N objects. For each of the N objects, the second biometric feature refers to the biometric feature used in the identity verification process proposed in this application embodiment; that is, the second biometric feature is not a biometric feature already stored for the object in the prior art. Each object may have one or more second biometric features. The second biometric feature is also referred to as a secondary biometric feature or sub-feature.

[0110] In some implementations, for any given object, the first and second biometric features can be biometric features extracted from biometric images of the object taken in different poses or environments. Assuming the object includes multiple second biometric features, these second biometric features can also be biometric features extracted from biometric images of the object taken in different poses or environments.

[0111] For example, assuming that the biometric feature provided in this application embodiment is a palm print feature, then the first biometric feature and multiple second biometric features of an object can be palm print features corresponding to palm print feature images collected from different angles.

[0112] For example, assuming that the biometric feature provided in this application embodiment is a palm print feature, then the first biometric feature and multiple second biometric features of an object can be palm print features corresponding to palm print feature images collected under unobstructed conditions and under conditions where a local area is obstructed, respectively.

[0113] For example, assuming that the biometrics provided in this application embodiment are facial features, then the first biometrics and multiple second biometrics of an object can be facial features corresponding to facial feature images captured from different angles.

[0114] For example, assuming that the biometrics provided in this application embodiment are facial features, then the first biometrics and the second biometrics of an object can be the facial features corresponding to the facial feature images collected when the object is not wearing a mask and when it is wearing a mask, respectively.

[0115] In some feasible implementations, the backend server can identify the identity information of the first object based on the current biometrics of the first object and the second biometrics of each of the N objects.

[0116] In some feasible implementations, for each of the N objects and for each second biometric feature of that object, the backend server can input the current biometric feature of the first object and the second biometric feature of that object into a fourth neural network model to obtain the similarity between the current biometric feature of the first object and the second biometric feature of that object, and identify the identity information of the first object based on the similarity and a third similarity threshold.

[0117] It should be understood that for each of the N objects, the backend server stores not only the object's second biometric feature, but also the object's identity information, where the object's identity information corresponds to the object's second biometric feature.

[0118] In some feasible implementations, for each of the N objects, if the similarity between the current biometric feature of the first object and the second biometric feature of the first object is greater than a third similarity threshold, then the backend server uses the identity information of the first object corresponding to the second biometric feature of the first object as the identity information of the first object.

[0119] In some feasible implementations, for each of the N objects, if the similarity between the current biometric feature of the first object and the second biometric feature of the first object is greater than or equal to a third similarity threshold, then the backend server uses the identity information of the object corresponding to the second biometric feature of the first object as the identity information of the first object.

[0120] In some implementations, the third similarity threshold may be less than or equal to the first similarity threshold mentioned above, but is not limited thereto.

[0121] In some implementations, the fourth neural network model can be a CNN, RNN, LSTM, GRU, BiLSTM, BiGRU, Transformer, BERT model, but is not limited to these.

[0122] In some feasible implementations, for each of the N objects, the backend server can input the current biometrics of the first object and the second biometrics of the object into the fifth neural network model to obtain a similarity score between the current biometrics of the first object and the second biometrics of the object, and identify the identity information of the first object based on the similarity score and the score threshold.

[0123] In some implementations, for each of the N objects, the backend server can use a fifth neural network model to calculate the similarity between the current biometrics of the first object and the second biometrics of the object, and determine the similarity score between the current biometrics of the first object and the second biometrics of the object based on the similarity.

[0124] In some implementation methods, the similarity interval [0,1] can be divided into multiple sub-intervals, each sub-interval corresponding to a similarity score. Based on this, the backend server uses the fifth neural network model to determine the similarity sub-intervals corresponding to the similarity between the current biometric feature of the first object and the second biometric feature of the object, and determines the similarity score corresponding to the sub-interval as the similarity score between the current biometric feature of the first object and the second biometric feature of the object.

[0125] For example, suppose the similarity [0,1] is divided into 10 sub-intervals, namely [0,0.1], (0.1,0.2], (0.2,0.3], (0.3,0.4], (0.4,0.5], (0.5,0.6], (0.6,0.7], (0.7,0.8], (0.8,0.9], (0.9,1], and their corresponding similarity scores are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.

[0126] In some possible implementations, for each of the N objects, if the similarity score between the current biometric feature of the first object and the second biometric feature of the first object is greater than a third score threshold, then the backend server uses the identity information of the object corresponding to the second biometric feature of the first object as the identity information of the first object.

[0127] In some possible implementations, for each of the N objects, if the similarity score between the current biometric feature of the first object and the second biometric feature of the first object is greater than or equal to a third score threshold, then the backend server uses the identity information of the object corresponding to the second biometric feature of the first object as the identity information of the first object.

[0128] In some implementations, the third scoring threshold may be less than or equal to the first scoring threshold mentioned above.

[0129] In some implementations, the fifth neural network model can be a CNN, RNN, LSTM, GRU, BiLSTM, BiGRU, Transformer, BERT model, but is not limited to these.

[0130] In some feasible implementations, the backend server can detect a second object among N objects that is suspected to be the first object; if a second object exists among the N objects and the second object has a second biometric feature, the backend server identifies the identity information of the first object based on the current biometric feature of the first object and the second biometric feature of the second object.

[0131] It should be noted that in this implementation, the backend server does not need to identify the identity information of the first object based on the current biometrics of the first object and the second biometrics of each object. Instead, it identifies the identity information of the first object based on the current biometrics of the first object and the second biometrics of the second object, thereby improving the efficiency of identity recognition.

[0132] In some implementation methods, the backend server can reduce the first similarity threshold used to identify the identity information of the first object based on the current biometrics of the first object and the first biometrics of each of the N objects, and obtain a second similarity threshold; calculate the similarity between the current biometrics of the first object and the first biometrics of each of the N objects; and determine the object among the N objects whose similarity is greater than the second similarity threshold as the second object.

[0133] It should be understood that the backend server can determine the adjustment range of the first similarity threshold based on the actual situation, and try to ensure that the second object selected using the second similarity threshold is the first object.

[0134] For example, suppose the first similarity threshold is 0.9 and the second similarity threshold is 0.8. Suppose that the similarity between the current biometrics of the first object and the first biometrics of each of the N objects is less than 0.9. When the first similarity threshold is reduced, suppose that the similarity between the current biometrics of the first object and the first biometrics of object 1 among the N objects is greater than 0.8. Then object 1 can be identified as the first object.

[0135] In some implementation methods, the backend server can reduce the first scoring threshold used to identify the identity information of the first object based on the current biometrics of the first object and the first biometrics of each of the N objects, and obtain a second scoring threshold; calculate the similarity between the current biometrics of the first object and the first biometrics of each of the N objects, determine the similarity scores corresponding to these similarities, and identify the objects among the N objects whose similarity scores are greater than the second scoring threshold as the second object.

[0136] It should be understood that the backend server can determine the adjustment range of the second scoring threshold based on the actual situation, in order to ensure as much as possible that the second object selected using the second scoring threshold is the first object.

[0137] For example, suppose the first scoring threshold is 9 and the second scoring threshold is 8. Suppose that the similarity score between the current biometrics of the first object and the first biometrics of each of the N objects is less than 9. When the first scoring threshold is reduced, suppose that the similarity score between the current biometrics of the first object and the first biometrics of object 1 among the N objects is greater than 8. Then object 1 can be identified as the first object.

[0138] In some implementation methods, if the second object has a second biometric feature, the backend server calculates the similarity between the current biometric feature of the first object and the second biometric feature of the second object; based on the similarity between the current biometric feature of the first object and the second biometric feature of the second object, the identity information of the first object is identified.

[0139] In some possible implementations, the identity information of the first object is identified based on the similarity between the current biometric feature of the first object and the second biometric feature of the second object, including: if the similarity between the current biometric feature of the first object and the second biometric feature is greater than a third similarity threshold, then the backend server uses the identity information of the object corresponding to the second biometric feature as the identity information of the first object.

[0140] In some possible implementations, the identity information of the first object is identified based on the similarity between the current biometric feature of the first object and the second biometric feature of the second object. This includes: the backend server calculates the similarity score between the current biometric feature of the first object and the second biometric feature; if the similarity score between the current biometric feature of the first object and the second biometric feature is greater than a third score threshold, then the backend server uses the identity information of the object corresponding to the second biometric feature as the identity information of the first object.

[0141] It should be noted that if the second object has a second biometric feature, the way the backend server identifies the identity information of the first object based on the current biometric feature of the first object and the second biometric feature of the second object can be understood as a 1v1 identity recognition method based on the second biometric feature. The requirements of the 1v1 identity recognition method are usually slightly lower than those of the 1vN identity recognition method. Therefore, as mentioned above, the third similarity threshold can be less than the first similarity threshold, and the third score threshold can be less than the first score threshold.

[0142] In some possible implementations, if the second object has multiple second biometric features, the backend server calculates the similarity between the current biometric feature of the first object and each second biometric feature of the second object; based on the similarity between the current biometric feature of the first object and each second biometric feature of the second object, the identity information of the first object is identified.

[0143] In some possible implementations, the identity information of the first object is identified based on the similarity between the current biometrics of the first object and each second biometric of the second object, including: if the similarity between the current biometrics of the first object and any second biometric of the second object is greater than a third similarity threshold, then the backend server uses the identity information of the object corresponding to the second biometric as the identity information of the first object.

[0144] For example, suppose the backend server stores two second biometric features of object 1: palm print features when the palm is obscured in the lower left corner and palm print features when the palm is obscured in the lower right corner. Both of these biometric features correspond to object 1's identity information. Suppose the backend server obtains object 1's current palm print features, and the similarity between the current palm print features and the palm print features when the palm is obscured in the lower left corner is 90%, which is greater than the third similarity threshold of 80%. Then, the backend server can determine object 1's identity information based on the correspondence between the palm print features when the palm is obscured in the lower left corner and object 1's identity information.

[0145] For example, suppose the backend server stores two second biometric features of object 1: facial features when wearing a mask and facial features when wearing glasses. Both of these biometric features correspond to object 1's identity information. Suppose the backend server obtains object 1's current facial features, and the similarity between the current facial features and the facial features when wearing a mask is 90%, which is greater than the third similarity threshold of 80%. Then, the backend server can determine object 1's identity information based on the correspondence between the facial features when wearing a mask and object 1's identity information.

[0146] In some possible implementations, the identity information of the first object is identified based on the similarity between the current biometrics of the first object and each second biometric of the second object, including: if the similarity score between the current biometrics of the first object and any second biometric of the second object is greater than a third score threshold, then the backend server uses the identity information of the object corresponding to the second biometric as the identity information of the first object.

[0147] For example, suppose the backend server stores two second biometric features of object 1: palmprint features when the lower left side of the palm is obscured, and palmprint features when the lower right side of the palm is obscured. Both of these biometric features correspond to object 1's identity information. Suppose the backend server obtains object 1's current palmprint features, and the similarity score between the current palmprint features and the palmprint features when the lower left side of the palm is obscured is 9, which is greater than the third score threshold of 8. Then, the backend server can determine object 1's identity information based on the correspondence between the palmprint features when the lower left side of the palm is obscured and object 1's identity information.

[0148] For example, suppose the backend server stores two second biometric features of object 1: facial features when wearing a mask and facial features when wearing glasses. Both biometric features correspond to object 1's identity information. Suppose the backend server obtains object 1's current facial features, and the similarity score between the current facial features and the facial features when wearing a mask is 9, which is greater than the third score threshold of 8. Then, the backend server can determine object 1's identity information based on the correspondence between the facial features when wearing a mask and object 1's identity information.

[0149] In some implementation methods, if the second object has multiple second biometric features, the backend server can calculate the similarity between the current biometric feature of the first object and multiple second biometric features of the second object in descending order of the quality of the images collected for each of the multiple second biometric features. If the similarity between the current biometric feature of the first object and the target second biometric feature of the second object is greater than a third similarity threshold, the backend server will use the identity information of the second object as the identity information of the first object and stop calculating the similarity between the current biometric feature of the first object and other second biometric features of the second object.

[0150] It should be understood that in this implementation, the backend server does not need to calculate the similarity between each second biometric feature of the second object and the current biometric feature of the first object. Instead, it calculates the similarity between the second biometric feature of the second object and the current biometric feature of the first object according to the quality order of the biometric feature images corresponding to each second biometric feature from high to low. As long as there is a similarity between the current biometric feature of the first object and a certain second biometric feature of the second object that is greater than the third similarity threshold, the similarity calculation can be stopped, thereby improving the efficiency of identity recognition.

[0151] In some implementations, the backend server can input any biometric image into the neural network model to obtain the quality score of the biometric image. In order to distinguish this neural network model from the other neural network models mentioned above, this application may refer to the neural network model here as the sixth neural network model.

[0152] In some implementations, the sixth neural network model can be a CNN, RNN, LSTM, GRU, BiLSTM, BiGRU, Transformer, BERT model, but is not limited to these.

[0153] In some implementation methods, if the second object has multiple second biometric features, the backend server can calculate the similarity score between the current biometric feature of the first object and multiple second biometric features of the second object in descending order of the quality of the images collected for each of the multiple second biometric features. If the similarity score between the current biometric feature of the first object and the target second biometric feature of the second object is greater than a third score threshold, the backend server will use the identity information of the second object as the identity information of the first object and stop calculating the similarity score between the current biometric feature of the first object and other second biometric features of the second object.

[0154] It should be understood that in this implementation, the backend server does not need to calculate the similarity score between each second biometric feature of the second object and the current biometric feature of the first object. Instead, it calculates the similarity score between the second biometric feature of the second object and the current biometric feature of the first object according to the quality order of the biometric feature images corresponding to each second biometric feature from high to low. As long as there is a similarity score between the current biometric feature of the first object and a certain second biometric feature of the second object that is greater than the third score threshold, the similarity score calculation can be stopped, thereby improving the efficiency of identity recognition.

[0155] It should be understood that, in this embodiment, by relaxing the identity recognition conditions in the 1vN identity recognition process, an object suspected to be the first object can be identified. This allows for the determination of the object's second biometric feature, and a comparison is made between the first object's current biometric feature and its second biometric feature to identify the first object's identity information. In other words, by relaxing the identity recognition conditions in the 1vN identity recognition process, an object suspected to be the first object is first identified. Then, the object's biometric features in other postures or environments are determined, and the first object's current biometric feature is compared with its features in those other postures or environments to identify the first object's identity information. The principle behind this approach is that changes in the object's environment, posture, etc., often cause slight changes in the object's biometric features, making it impossible to identify the object's identity information using the current 1vN identity recognition method. However, even if the object's posture or environment changes, the biometric image captured in these situations is still the same object's biometric image. Therefore, the object's current biometric feature and its stored biometric feature are essentially consistent, and this consistency ensures the successful implementation of this application.

[0156] For example, suppose the backend server stores the palm print features of object 1 in both unobstructed and partially obstructed states. When the backend server obtains the current palm print features of object 1, it first uses a 1vN identity verification method to identify object 1. This includes comparing the current palm print features of object 1 with the palm print features of object 1 in the unobstructed state to identify object 1. If the identity verification of object 1 fails, and suppose the backend server detects object 1 by relaxing the identity verification conditions in the 1vN identity verification process, then the backend server can compare the current palm print features of object 1 with the stored palm print features in the partially obstructed state to identify object 1.

[0157] For example, suppose the backend server stores the facial features of object 1 with and without a mask. When the backend server obtains the current facial features of object 1, it first uses a 1vN identity verification method to identify object 1. This includes comparing the current facial features of object 1 with the facial features of object 1 without a mask. If the identity verification of object 1 fails, and suppose the backend server detects object 1 by relaxing the identity verification conditions in the 1vN verification process, then the backend server can compare the current facial features of object 1 with the stored facial features of object 1 with a mask to identify object 1.

[0158] In some implementations, if the second object does not exist among the N objects, the backend server can identify the identity information of the first object based on the terminal identifier of the first object.

[0159] In some possible implementations, if the second object does not have a second biometric feature, the backend server can identify the first object's identity information based on the first object's terminal identifier.

[0160] In some possible implementations, the terminal identifier of the first object can be the mobile phone number of the first object, such as the last four digits of the mobile phone number, but is not limited to this.

[0161] In some possible implementations, the identification of the first object based on the terminal identifier of the first object includes: the backend server detecting the activation status of the first object's biometric identification-related functions based on the terminal identifier of the first object; if the first object has activated the biometric identification-related functions, the backend server identifies the identity information of the first object based on the first object's current biometrics and the first biometrics of the first object.

[0162] In some possible implementations, the activation status of the biometric identification-related functions of the first object includes two scenarios: the first object has activated the biometric identification-related functions and the first object has not activated the biometric identification-related functions.

[0163] In some feasible implementations, the backend server identifies the identity information of the first object based on the first object's current biometrics and the first object's first biometrics, which refers to the current 1v1 identity recognition method.

[0164] In some implementation methods, the backend server identifies the identity information of the first object based on the similarity between the current biometric feature of the first object and the first biometric feature of the first object, including: if the similarity between the current biometric feature of the first object and the first biometric feature of the first object is greater than a fourth similarity threshold, then the backend server uses the identity information of the object corresponding to the first biometric feature as the identity information of the first object.

[0165] In some possible implementations, the backend server identifies the identity information of the first object based on the similarity between the current biometric feature of the first object and the first biometric feature of the first object, including: if the similarity between the current biometric feature of the first object and the first biometric feature of the first object is greater than or equal to a fourth similarity threshold, then the backend server uses the identity information of the object corresponding to the first biometric feature as the identity information of the first object.

[0166] In some implementations, the fourth similarity threshold is less than the first similarity threshold, but this is not the only possibility.

[0167] In some possible implementations, the backend server identifies the identity information of the first object based on the similarity between the current biometric feature of the first object and the first biometric feature of the first object, including: if the similarity score between the current biometric feature of the first object and the first biometric feature of the first object is greater than a fourth score threshold, then the backend server uses the identity information of the object corresponding to the first biometric feature as the identity information of the first object.

[0168] In some possible implementations, the backend server identifies the identity information of the first object based on the similarity between the current biometric feature of the first object and the first biometric feature of the first object, including: if the similarity score between the current biometric feature of the first object and the first biometric feature of the first object is greater than or equal to a fourth score threshold, then the backend server uses the identity information of the object corresponding to the first biometric feature as the identity information of the first object.

[0169] In some possible implementations, the fourth scoring threshold is less than the first scoring threshold, but this is not the only one.

[0170] In some feasible implementations, if the first object has enabled biometric recognition functions, and the backend server successfully identifies the first object's identity information based on the first object's current biometrics and its first biometrics, then the first object's current biometrics are used as its second biometrics and stored. In other words, the backend server can use the first object's current biometrics as its secondary biometrics and store it for subsequent identification of that object or other objects.

[0171] In some feasible implementations, as long as the first object has enabled biometric recognition functions, and the backend server successfully identifies the identity information of the first object based on the first object's current biometrics and the first object's first biometrics, the backend server will use the first object's current biometrics as the first object's second biometrics and store the first object's second biometrics.

[0172] In some other possible implementations, before the backend server uses the current biometrics of the first object as the second biometrics of the first object and stores the second biometrics of the first object, it can determine the quality score of the current biometric image corresponding to the current biometrics of the first object; accordingly, if the quality score of the current biometric image is greater than a preset score, the backend server uses the current biometrics of the first object as the second biometrics of the first object and stores the second biometrics of the first object.

[0173] It should be understood that this approach allows the backend server to store high-quality biometric data, thereby improving the accuracy of identity verification.

[0174] In some implementations, the backend server can input the currently acquired biometric image into a neural network model to obtain a quality score for the currently acquired biometric image.

[0175] As mentioned above, this neural network model can be referred to as the sixth neural network model.

[0176] In some implementations, the backend server can obtain training samples of the sixth neural network model; and train the sixth neural network model using the training samples of the sixth neural network model.

[0177] In some implementations, each training sample of the sixth neural network model may include: a biometric image to be scored and the actual quality score of the biometric image, wherein the actual quality score can serve as a sample label. The training device may employ supervised training; for example, it may input the biometric image to be scored into the sixth neural network model and output the predicted quality score corresponding to the biometric image. Further, the training device may calculate a loss based on the predicted and actual quality scores included in all training samples, and adjust the parameters of the sixth neural network model based on this loss until the training iterations reach a preset number or the loss reaches its minimum value, at which point training stops.

[0178] In some implementations, the training device may use any of the following loss functions when training the sixth neural network model, but is not limited to: L1 loss function, mean squared error (MSE) loss function, cross-entropy loss function, etc.

[0179] For example, Figure 3 is a schematic diagram of the sixth neural network model provided in the embodiment of this application. As shown in Figure 3, the sixth neural network model may include: convolutional layers, residual layers and fully connected layers.

[0180] For example, the loss function of the sixth neural network model can be as follows:

[0181] Where X represents the biometric image, f(·) represents the objective function, Y represents the actual quality score corresponding to the biometric image, which serves as the sample label, and M-1 represents the number of samples.

[0182] In some feasible implementations, the sample labels for each training sample can be manually labeled or labeled automatically. The following explains the automatic labeling method:

[0183] In some implementations, the training device can acquire M biometric images of a third object, where M is an integer greater than 1; select the highest quality target biometric image from the M biometric images; calculate the similarity between the target biometric image and the other M-1 biometric images (excluding the target biometric image); and combine the similarity scores of the other M-1 biometric images and the target biometric image to form M-1 training samples for the neural network model. In other words, each training sample of the neural network model includes: a biometric image and its similarity score to the target biometric image, which can be used as the actual quality score of the biometric image.

[0184] In some possible implementations, the backend server can use a quality recognition module or software to select the highest quality target biometric image from the M biometric images, but is not limited to this.

[0185] For example, Figure 4 is a schematic diagram of biometric image selection provided in an embodiment of this application. As shown in Figure 4, the biometric image library can store multiple biometric images of at least one object. The backend server can use a quality recognition module or software to identify the highest quality biometric image and the remaining biometric images of each object. In Figure 4, taking an object as an example that includes M biometric images, the backend server can use a quality recognition module or software to identify the highest quality biometric image and the remaining M-1 biometric images of the object.

[0186] In some feasible implementations, the backend server can use a similarity calculation network or module to calculate the similarity between the target biometric image and the other M-1 biometric images (excluding the target biometric image) among the M biometric images. The similarity between the other M-1 biometric images and the target biometric image is then used to form the M-1 training samples of the neural network model.

[0187] For example, Figure 5 is a schematic diagram of the generation of training samples provided in the embodiment of this application. As shown in Figure 5, the backend server can use a similarity calculation network or module to convert the target biometric image of an object into an embedding vector, and convert the remaining M-1 biometric images of the object into M-1 embedding vectors. Further, the Eulerian distance between the embedding vector corresponding to the target biometric image and each of the M-1 embedding vectors can be calculated. The M-1 Eulerian distances are normalized to obtain the quality scores corresponding to the M-1 biometric images, which are the similarities between the other M-1 biometric images and the target biometric image. Finally, the similarities between the other M-1 biometric images and the target biometric image are used to form M-1 training samples of the neural network model, and these training samples are stored in the training database.

[0188] For example, Figure 6 is a schematic diagram of preprocessing and model training provided in an embodiment of this application. As shown in Figure 6, the preprocessing process includes: the biometric image library can store multiple biometric images of at least one object; the backend server can use a quality recognition module or software to identify the highest quality biometric image of the object and the remaining M-1 biometric images of the object; the backend server can use a similarity calculation network or module to convert the target biometric image of an object into an embedding vector, and convert the remaining M-1 biometric images of the object into M-1 embedding vectors; further, the Euclidean distance between the embedding vector corresponding to the target biometric image and each of the M-1 embedding vectors can be calculated, and the M-1 Euclidean distances can be normalized to obtain the similarity between the other M-1 biometric images and the target biometric image; finally, the similarity between the other M-1 biometric images and the target biometric image is used to form M-1 training samples for the neural network model, and these training samples are stored in the training database. The model training process includes: the backend server can use the training samples in the training database to train the sixth neural network model.

[0189] It should be understood that the embodiments of this application can calculate the similarity between M-1 biometric images of the same object under different postures or environments and the highest quality biometric image of that object. For any biometric image among the M-1 biometric images, the higher the similarity, the higher the quality of the biometric image; the lower the similarity, the lower the quality. The role of the sixth neural network model is to select biometric images of the same object under different postures or environments. Therefore, the training samples with the aforementioned automated annotation are consistent with the role of the sixth neural network model. These training samples can be used to train the sixth neural network model, enabling it to select biometric images of the same object under different postures or environments, and thus allowing these biometric images to be applied in the subsequent identity recognition process.

[0190] It should be understood that the storage method of the second biometric features of the above N objects is the same as that of the first object. In both cases, after the object's identity information is successfully identified through the current 1v1 identity recognition method, the object's current biometric features are used as the second biometric features and stored.

[0191] In some possible implementations, if the first object has not enabled biometric recognition functions, the backend server can trigger the first object to enable biometric recognition functions.

[0192] In some implementation methods, if the first object has not enabled biometric recognition functions, the backend server can use the first object's current biometrics as its first biometrics and store it. In other words, if the first object is a new object, the backend server can use its first biometrics as its primary biometrics and store it.

[0193] In some implementations, embodiments of this application may limit the number of second biometric features for each object to reduce the complexity of the identity recognition system.

[0194] For example, a backend server can store up to five secondary biometrics of an object. If the number of secondary biometrics exceeds this limit, the backend server can choose to delete some of the secondary biometrics.

[0195] In some possible implementations, if the identification of the first object fails based on the current biometrics of the first object and the second biometrics of each of the N objects, then the identification of the first object is performed based on the terminal identifier of the first object.

[0196] In other words, if the identity verification method provided in this application fails to identify the identity information of the first object, then the backend server will identify the identity information of the first object based on the terminal identifier of the first object. For details on how to identify the identity information of the first object based on its terminal identifier, please refer to the above; this application will not elaborate further.

[0197] This application provides an identity recognition method, including: obtaining the current biometric features of a first object; recognizing the identity information of the first object based on the current biometric features of the first object and the first biometric features of N objects; wherein N is a positive integer; if the recognition of the identity information of the first object based on the current biometric features of the first object and the first biometric features of N objects fails, then the identity information of the first object is recognized based on the current biometric features of the first object and the second biometric features of N objects. Since when the 1vN identity recognition method fails to recognize the first object, it does not directly enter the current 1v1 phone number verification stage, but instead adds a stage for identity verification through the second biometric feature. Only when the verification through this stage fails does it enter the current 1v1 phone number verification stage. When the recognition through the second biometric feature is successful, there is no need to enter the 1v1 phone number verification stage. The identity verification stage provided in this application is an automated stage, which has a shorter recognition time compared to the 1v1 phone number verification stage. Therefore, the identity recognition method provided in this application can shorten the identity recognition time and eliminate the need for UI interaction by the object, ultimately improving the user experience.

[0198] It should be understood that, for any given object, the first and second biometric features can be biometric features extracted from biometric images of the object taken in different poses or environments. Therefore, the essential meaning of the identity recognition method provided in this application is that the backend server can store the biometric features of the same object in different poses or environments. During identity recognition, the backend server can compare the current biometric features of the same object with those in different poses or environments to identify the object's identity information. This approach can improve the accuracy of identity recognition.

[0199] The following example illustrates the identity recognition method provided in this application:

[0200] For example, Figure 7 is a flowchart of another identity recognition method provided in an embodiment of this application. As shown in Figure 7, the method can be a backend server corresponding to an application that implements biometric recognition-related functions. For example, the backend server can be a payment backend server, a membership points mini-program, or a power bank rental mini-program, but it is not limited to these. As shown in Figure 7, the method can include:

[0201] S701: Obtain the current biometric characteristics of the first object;

[0202] It should be understood that the explanation of S701 can be found in the explanation of S210, and will not be repeated in this embodiment.

[0203] S702: Based on the current biometrics of the first object and the first biometrics of each of the N objects, identify the identity information of the first object; where N is a positive integer;

[0204] It should be understood that the explanation of S702 can be found in the explanation of S220, and will not be repeated in this embodiment.

[0205] S703: Determine whether the identity information of the first object has been successfully recognized; if the recognition is successful, execute S704; if the recognition fails, execute S705.

[0206] S704: Execute subsequent tasks based on the identity information of the first object;

[0207] In some feasible ways, the follow-up tasks can be payment tasks, membership points tasks, or free rental of power banks, etc., but are not limited to these.

[0208] S705: Relax the identity recognition conditions of the 1vN identity recognition method, and determine whether there is a second object among the N objects that is suspected to be the first object; if there is a second object, then execute S706; if there is no second object, then execute S709.

[0209] In some possible implementations, the relaxation of the identification conditions and the determination of the second object can be referred to the explanation below S230, and will not be repeated in this embodiment.

[0210] S706: Determine whether the second object has a second biometric feature; if the second biometric feature exists, execute S707; if it does not exist, execute S709.

[0211] It should be understood that the explanation of the second biometric feature can be found in the explanation of S230, and will not be repeated in this embodiment.

[0212] S707: Identify the identity information of the first object based on the current biometrics of the first object and the second biometrics of the second object;

[0213] It should be understood that the explanation of S770 can be found in the explanation of S230, and will not be repeated in this embodiment.

[0214] S708: Determine whether the identity information of the first object has been successfully recognized; if the recognition is successful, execute S704; if the recognition fails, execute S709.

[0215] S709: Obtain the terminal identifier of the first object;

[0216] S710: Detect the activation status of biometric identification related functions of the first object based on the terminal identifier of the first object; if the first object has activated the biometric identification related functions, then execute S711; if the first object has not activated the biometric identification related functions, then execute S714.

[0217] It should be understood that the terminal identifier of the first object corresponds to the activation status of the biometric identification-related functions of the first object.

[0218] S711: Identify the identity information of the first object based on the current biometrics of the first object and the first biometrics of the first object;

[0219] It should be understood that the identity recognition method involved in S711 is the current 1v1 identity recognition method. For an explanation of this method, please refer to the above text. This application embodiment will not repeat it here.

[0220] S712: Determine whether the identity information of the first object has been successfully recognized; if the recognition is successful, then execute S713;

[0221] In some feasible methods, if the identity information of the first object fails to be identified through 1v1 identification, the backend server can trigger the terminal device to re-collect the biometric image of the first object, but this is not limited to this.

[0222] S713: Use the current biometrics of the first object as the second biometrics of the first object, and store the second biometrics of the first object;

[0223] It should be understood that the explanation of S713 can be found in the explanation below S230, and will not be repeated in this embodiment.

[0224] S714: Trigger the first object to enable biometric recognition related functions, and use the current biometrics of the first object as the first biometrics of the first object, and store the first biometrics of the first object.

[0225] It should be understood that the explanation of S714 can be found in the explanation below S230, and will not be repeated in this embodiment.

[0226] The identity recognition method provided in this application avoids directly entering the 1v1 phone number verification stage after a 1vN identity recognition failure. Instead, it adds a second biometric verification step. The 1v1 phone number verification stage only begins if the second biometric verification fails. If the second biometric verification is successful, the 1v1 phone number verification stage is unnecessary, thus shortening the identity recognition time and eliminating the need for UI interaction, ultimately improving the user experience. Furthermore, it avoids the high training costs and incomplete coverage issues associated with identity recognition methods that rely on augmented data training.

[0227] The preferred embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this application, various simple modifications can be made to the technical solutions of this application, and these simple modifications all fall within the protection scope of this application. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this application will not describe the various possible combinations separately. Furthermore, various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the spirit of this application, they should also be considered as the content disclosed in this application.

[0228] It should also be understood that, in the various method embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0229] The method provided in the embodiments of this application has been described above. The identity recognition device provided in the embodiments of this application will be described below.

[0230] Figure 8 is a schematic diagram of an identity recognition device 800 provided in an embodiment of this application. As shown in Figure 8, the device 800 includes an acquisition module 810 and an identity recognition module 820. The acquisition module 810 is used to acquire the current biometric features of a first object. The identity recognition module 820 is used to: recognize the identity information of the first object based on the current biometric features of the first object and the first biometric features of N objects; where N is a positive integer; if the recognition of the identity information of the first object based on the current biometric features of the first object and the first biometric features of N objects fails, then the identity information of the first object is recognized based on the current biometric features of the first object and the second biometric features of N objects.

[0231] In some implementations, the identity recognition module 820 is specifically used to: recognize the identity information of the first object based on the current biometrics of the first object and the second biometrics of each of the N objects.

[0232] In some implementation methods, the identity recognition module 820 is specifically used to: detect a second object among N objects that is suspected to be the first object; if there is a second object among the N objects and the second object has a second biometric feature, then the identity information of the first object is recognized based on the current biometric feature of the first object and the second biometric feature of the second object.

[0233] In some implementations, the identity recognition module 820 is specifically used to: reduce the first similarity threshold used when recognizing the identity information of the first object based on the current biometrics of the first object and the first biometrics of each of the N objects, to obtain a second similarity threshold; calculate the similarity between the current biometrics of the first object and the first biometrics of each of the N objects; and identify the object among the N objects whose similarity is greater than the second similarity threshold as the second object.

[0234] In some implementations, the identity recognition module 820 is specifically used to: if the second object has multiple second biometric features, calculate the similarity between the current biometric feature of the first object and each second biometric feature of the second object; and recognize the identity information of the first object based on the similarity between the current biometric feature of the first object and each second biometric feature of the second object.

[0235] In some implementations, the identity recognition module 820 is specifically used to: if the second object has multiple second biometric features, calculate the similarity between the current biometric feature of the first object and multiple second biometric features of the second object in descending order of quality of the biometric feature images corresponding to each of the multiple second biometric features; if the similarity between the current biometric feature of the first object and the target second biometric feature of the second object is greater than a third similarity threshold, use the identity information of the second object as the identity information of the first object, and stop calculating the similarity between the current biometric feature of the first object and other second biometric features of the second object.

[0236] In some implementations, the identity recognition module 820 is also used to: if there is no second object among the N objects, then recognize the identity information of the first object based on the terminal identifier of the first object.

[0237] In some implementations, the identity recognition module 820 is also used to: if the second object does not have a second biometric feature, then identify the identity information of the first object based on the terminal identifier of the first object.

[0238] In some implementation methods, the identity recognition module 820 is specifically used to: detect the activation status of the biometric recognition-related functions of the first object based on the terminal identifier of the first object; if the first object has activated the biometric recognition-related functions, then identify the identity information of the first object based on the current biometrics of the first object and the first biometrics of the first object.

[0239] In some implementations, the device 800 further includes a storage model 830 for: if the identity information of the first object is successfully identified based on the current biometrics of the first object and the first biometrics of the first object, then the current biometrics of the first object is used as the second biometrics of the first object, and the second biometrics of the first object is stored.

[0240] In some implementations, the device 800 further includes a determination model 840, used to determine the quality score of the current biometric image corresponding to the current biometric of the first object before the storage model 830 uses the current biometric of the first object as the second biometric of the first object and stores the second biometric of the first object; correspondingly, the storage model 830 is specifically used to: if the quality score of the current biometric image is greater than a preset score, then use the current biometric of the first object as the second biometric of the first object and store the second biometric of the first object.

[0241] In some possible implementations, model 840 is specifically used to: input the current biometric image into a neural network model to obtain a quality score for the current biometric image.

[0242] In some implementations, the device 800 further includes a training model 850, and the acquisition module 810 is also used to acquire training samples of the neural network model; the training model 850 is used to train the neural network model using the training samples of the neural network model.

[0243] In some implementations, the acquisition module 810 is specifically used to: acquire M biometric images of the third object; where M is an integer greater than 1; select the target biometric image with the highest quality among the M biometric images; calculate the similarity between the other M-1 biometric images (excluding the target biometric image) and the target biometric image; and form M-1 training samples for the neural network model by combining the similarity between the other M-1 biometric images and the target biometric image.

[0244] In some implementations, the device 800 also includes a triggering model 860, wherein if the first object has not enabled biometric identification-related functions, the triggering model 860 is used to trigger the first object to enable biometric identification-related functions.

[0245] In some implementations, if the first object has not enabled biometric identification functions, the storage model 830 is also used to use the current biometrics of the first object as the first biometrics of the first object and store the first biometrics of the first object.

[0246] In some implementations, the acquisition module 810 is specifically used to: acquire the current biometric image of the first object; and extract features from the current biometric image to obtain the current biometric features of the first object.

[0247] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, they will not be repeated here. Specifically, the device 800 shown in FIG8 can execute the method embodiments corresponding to FIG2 and FIG7, and the foregoing and other operations and / or functions of each module in the device 800 are respectively to implement the corresponding processes in the methods in FIG2 and FIG7. For the sake of brevity, they will not be repeated here.

[0248] The apparatus 800 of this application embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, the steps of the method embodiments in this application can be completed by integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the method disclosed in this application embodiment can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiments.

[0249] Figure 9 is a schematic block diagram of an electronic device 900 provided in an embodiment of this application. The electronic device 900 may be the aforementioned backend server, but is not limited thereto. As shown in Figure 9, the electronic device 900 may include:

[0250] The system includes a memory 910 and a processor 920. The memory 910 stores a computer program 930 and transfers the computer program 930 to the processor 920. In other words, the processor 920 can retrieve and run the computer program 930 from the memory 910 to implement the methods described in the embodiments of this application.

[0251] For example, the processor 920 can be used to execute the steps in the above method according to the instructions in the computer program 930.

[0252] In some embodiments of this application, the processor 920 may include, but is not limited to:

[0253] General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0254] In some embodiments of this application, the memory 910 includes, but is not limited to:

[0255] Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0256] In some embodiments of this application, the computer program 930 may be divided into one or more modules, which are stored in the memory 910 and executed by the processor 920 to perform the method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 930 in the electronic device.

[0257] As shown in Figure 9, the electronic device 900 may further include:

[0258] Transceiver 940, which can be connected to processor 920 or memory 910.

[0259] The processor 920 can control the transceiver 940 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 940 may include a transmitter and a receiver. The transceiver 940 may further include antennas, and the number of antennas may be one or more.

[0260] It should be understood that the various components in the electronic device 900 are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0261] According to one aspect of this application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a computer, enables the computer to perform the methods of the above-described method embodiments. Alternatively, embodiments of this application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the methods of the above-described method embodiments.

[0262] According to another aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method described in the above-described method embodiments.

[0263] In other words, when implemented using software, it can be implemented wholly or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0264] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in 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 implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0265] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0266] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment, depending on actual needs. For example, the functional modules in the various embodiments of this application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0267] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An identity recognition method, characterized by, The method comprises: acquiring a current biometric feature of a first object; identifying identity information of the first object based on the current biometric feature and first biometric features of N objects respectively; wherein N is a positive integer; if the identity information of the first object is not identified based on the current biometric feature and the first biometric features of the N objects respectively, then identifying the identity information of the first object based on the current biometric feature and second biometric features of the N objects respectively.

2. The method of claim 1, wherein, The step of identifying the identity information of the first object based on the current biometric feature and the second biometric features of the N objects respectively comprises: identifying the identity information of the first object based on the current biometric feature and the second biometric feature of each of the N objects.

3. The method of claim 1, wherein, The step of identifying the identity information of the first object based on the current biometric feature and the second biometric features of the N objects respectively comprises: detecting a second object of the N objects that is suspected to be the first object; if the second object exists in the N objects and has a second biometric feature, then identifying the identity information of the first object based on the current biometric feature and the second biometric feature of the second object.

4. The method of claim 3, wherein, The step of detecting the second object of the N objects that is suspected to be the first object comprises: adjusting a first similarity threshold used for identifying the identity information of the first object based on the current biometric feature and the first biometric features of the N objects respectively to obtain a second similarity threshold; calculating similarities between the current biometric feature and the first biometric features of the N objects respectively; determining, as the second object, an object of the N objects whose similarity is greater than the second similarity threshold.

5. The method of claim 3, wherein, The step of detecting the second object of the N objects that is suspected to be the first object comprises: adjusting a first score threshold used for identifying the identity information of the first object based on the current biometric feature and the first biometric features of the N objects respectively to obtain a second score threshold; calculating similarities between the current biometric feature and the first biometric features of the N objects respectively, and determining a similarity score corresponding to each similarity; determining, as the second object, an object of the N objects whose similarity score is greater than the second score threshold.

6. The method according to any one of claims 3-5, characterized in that, The step of identifying the identity information of the first object based on the current biometric feature and the second biometric feature of the second object comprises: if the second object has multiple second biometric features, then calculating similarities between the current biometric feature and each second biometric feature of the second object; identifying the identity information of the first object based on the similarities between the current biometric feature and each second biometric feature of the second object.

7. The method according to any one of claims 3-5, characterized in that, The step of identifying the identity information of the first object based on the current biometric feature and the second biometric feature of the second object comprises: If the second object has multiple second biological features, the similarity between the current biological feature and the multiple second biological features of the second object is calculated in order of quality of the biological feature collection images corresponding to the multiple second biological features from high to low; If the similarity between the current biological feature and the target second biological feature of the second object is greater than a third similarity threshold, the identity information of the second object is taken as the identity information of the first object, and the similarity between the current biological feature and other second biological features of the second object is stopped from being calculated.

8. The method according to any one of claims 3-5, characterized in that, Further comprising: If the second object does not exist in the N objects, the identity information of the first object is identified based on the terminal identifier of the first object.

9. The method according to any one of claims 3-5, characterized in that, Further comprising: If the second object does not have a second biological feature, the identity information of the first object is identified based on the terminal identifier of the first object.

10. The method according to any one of claims 1 to 9, characterized in that, The identity information of the first object is identified based on the terminal identifier of the first object, comprising: Detecting the opening of the biological feature recognition related function of the first object based on the terminal identifier of the first object; If the first object has opened the biological feature recognition related function, the identity information of the first object is identified based on the current biological feature and the first biological feature of the first object.

11. The method of claim 10, wherein, Further comprising: If the identity information of the first object is successfully identified based on the current biological feature and the first biological feature of the first object, the current biological feature is taken as the second biological feature of the first object, and the second biological feature of the first object is stored.

12. The method of claim 11, wherein, Before the current biological feature is taken as the second biological feature of the first object and the second biological feature of the first object is stored, further comprising: Determining the quality score of the current biological feature collection image corresponding to the current biological feature; The current biological feature is taken as the second biological feature of the first object and the second biological feature of the first object is stored, comprising: If the quality score of the current biological feature collection image is greater than a preset score, the current biological feature is taken as the second biological feature of the first object, and the second biological feature of the first object is stored.

13. The method of claim 12, wherein, The quality score of the current biological feature collection image corresponding to the current biological feature is determined, comprising: Inputting the current biological feature collection image into a neural network model to obtain the quality score of the current biological feature collection image.

14. The method of claim 13, wherein, Further comprising: Obtaining training samples of the neural network model; Training the neural network model through the training samples of the neural network model.

15. The method of claim 14, wherein, The training samples of the neural network model are obtained, comprising: Obtaining M biological feature images of a third object; wherein M is an integer greater than 1; Selecting a target biological feature image with the highest quality from the M biological feature images; Calculating the similarity between the M-1 biological feature images other than the target biological feature image and the target biological feature image respectively; The other M-1 biological feature images and the similarity of each of the other M-1 biological feature images to the target biological feature image constitute M-1 training samples of the neural network model.

16. The method of claim 10, wherein, Further comprising: If the first object does not open the biological feature recognition related function, triggering the first object to open the biological feature recognition related function.

17. The method of claim 10, wherein, Further comprising: If the first object does not open the biological feature recognition related function, the current biological feature is taken as the first biological feature of the first object, and the first biological feature of the first object is stored.

18. The method according to any one of claims 1 to 17, characterized in that, The current biological feature of the first object is obtained, comprising: Obtaining a current biological feature collection image of the first object; Performing feature extraction on the current biological feature collection image to obtain the current biological feature.

19. An identity recognition apparatus, characterized by comprising: Comprising: An obtaining module, configured to obtain a current biological feature of a first object; An identity recognition module, configured to: Recognize identity information of the first object based on the current biological feature and first biological features of N objects respectively; wherein N is a positive integer; If the identity information of the first object is not recognized based on the current biological feature and the first biological features of the N objects respectively, then recognize the identity information of the first object based on the current biological feature and second biological features of the N objects respectively.

20. An electronic device, comprising: Comprising: A processor and a memory, the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method in any one of claims 1 to 18.

21. A computer-readable storage medium, characterized in that, A computer program for storing, the computer program causes a computer to execute the method in any one of claims 1 to 18.

22. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instruction is executed by the processor to implement the method in any one of claims 1 to 18.

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