WO2021139493A1 - Visitor identity authentication method and apparatus based on machine learning, and computer device - Google Patents

Abstract

Disclosed are a visitor identity authentication method and apparatus based on machine learning, and a computer device, which relate to the technical field of information, and are mainly aimed at effectively authenticating the identity of a visitor, improving the authentication precision of the identity of the visitor, and ensuring the efficiency of authenticating the identity of the visitor. The method comprises: acquiring facial features of a visitor to be authenticated; performing multi-level analysis according to the facial features of the visitor to be authenticated and a plurality of facial features respectively corresponding to each visitor in a preset feature library, and determining each target visitor in the preset feature library according to a multi-level analysis result; and determining, according to the plurality of facial features respectively corresponding to each target visitor, the identity of the visitor to be authenticated. The present method relates to machine learning technology in artificial intelligence, is applicable to visitor identity authentication, and also relates to blockchain technology.

Description

Machine learning-based visitor identity authentication method, device and computer equipment

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on July 31, 2020, the application number is 202010761117.7, and the invention title is "Machine Learning-based Visitor Identity Authentication Method, Apparatus, and Computer Equipment". The entire content is approved The reference is incorporated in this application.

Technical field

This application relates to the field of information technology, and in particular to a visitor identity authentication method, device and computer equipment based on machine learning.

Background technique

Many scenes now involve visitor registration, for example, common interview invitations, business negotiations between different companies, business trips and exchanges between branches and head offices, registration of outsiders from schools and communities, and visitors can be registered through visitor registration. Personnel identity is verified.

At present, when authenticating the identity of a visitor, a visitor's photo is usually collected, and the characteristics of the collected visitor's photo are compared with the characteristics of all visitors in the feature database, and the visitor's identity is authenticated according to the comparison result. However, the inventor realizes that only a single feature of each visitor is usually stored in the feature library, and only the extracted visitor feature is compared with a single feature of a visitor in the feature library, which cannot ensure the accuracy of the comparison result. The accuracy of identity authentication is low. If multiple characteristics of each visitor are extracted and stored in the signature database, the comparison workload will be increased when the identity of the visitor is authenticated, thereby affecting the authentication efficiency of the visitor's identity.

technical problem

Mainly lies in being able to carry out effective authentication to the visitor's identity, while improving the visitor's identity authentication accuracy, can guarantee the visitor identity authentication efficiency.

Technical solutions

This application provides a visitor identity authentication method, device, and computer equipment based on machine learning, which are mainly capable of effectively authenticating the visitor's identity. While improving the accuracy of the visitor's identity authentication, it can ensure the authentication efficiency of the visitor's identity.

According to the first aspect of this application, a visitor identity authentication method based on machine learning is provided, including:

Obtain the facial features of the visitors to be authenticated;

Perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and determine each target visitor in the preset feature library according to the multi-level analysis result;

The identity of the visitor to be authenticated is determined according to the multiple facial features corresponding to each target visitor.

According to the second aspect of this application, a visitor identity authentication device based on machine learning is provided, including:

The obtaining unit is used to obtain the facial features of the visitor to be authenticated;

The analysis unit is configured to perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and to determine the preset feature library according to the multi-level analysis result Each target visitor;

The determining unit is configured to determine the identity of the visitor to be authenticated according to multiple facial features corresponding to each target visitor.

According to a third aspect of the present application, there is provided a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented:

Obtain the facial features of the visitors to be authenticated;

Perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and determine each target visitor in the preset feature library according to the multi-level analysis result;

The identity of the visitor to be authenticated is determined according to the multiple facial features corresponding to each target visitor.

According to a fourth aspect of the present application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the following steps when the program is executed:

Obtain the facial features of the visitors to be authenticated;

Perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and determine each target visitor in the preset feature library according to the multi-level analysis result;

The identity of the visitor to be authenticated is determined according to the multiple facial features corresponding to each target visitor.

Beneficial effect

This application provides a machine learning-based visitor identity authentication method, device, and computer equipment. Compared with the current method of comparing the extracted visitor characteristics with the single characteristic of the visitor in the feature database, this application can obtain the information of the visitor to be authenticated. Face features; and perform multi-level analysis based on the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, so as to exclude the preset feature library and the waiting list according to the multi-level analysis result The vast majority of visitors who are not close to the authenticated visitors will only keep a few target visitors close to the visitors to be authenticated, and then determine the identity of the visitor to be authenticated according to the multiple facial features corresponding to each target visitor. The multiple facial features of the target visitor in the preset feature library are used to authenticate the identity of the visitor to be authenticated, which improves the accuracy of the visitor's identity verification. At the same time, it can also use multi-level analysis to exclude the face of most visitors in the preset feature library. Features greatly reduce the workload of feature comparison. While improving the accuracy of visitor identity authentication, it can ensure the efficiency of visitor identity authentication.

Description of the drawings

The drawings described here are used to provide a further understanding of the application and constitute a part of the application. The exemplary embodiments and descriptions of the application are used to explain the application, and do not constitute an improper limitation of the application. In the attached picture:

Fig. 1 shows a flow chart of a method for guest identity authentication based on machine learning provided by an embodiment of the present application;

FIG. 2 shows a flowchart of another method for guest identity authentication based on machine learning provided by an embodiment of the present application;

FIG. 3 shows a schematic structural diagram of a visitor identity authentication device based on machine learning provided by an embodiment of the present application;

FIG. 4 shows a schematic structural diagram of another visitor identity authentication device based on machine learning provided by an embodiment of the present application;

Fig. 5 shows a schematic diagram of the physical structure of a computer device provided by an embodiment of the present application.

Embodiments of the present invention

Hereinafter, the application will be described in detail with reference to the drawings and in conjunction with the embodiments. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other if there is no conflict.

At present, when authenticating the identity of a visitor, only the extracted visitor feature is compared with a single feature of the visitor in the feature database. The accuracy of the comparison result cannot be ensured. The authentication accuracy of the visitor's identity is low. Collecting multiple characteristics of each visitor will increase the workload of feature comparison, thereby affecting the efficiency of visitor identity authentication.

In order to solve the foregoing problem, an embodiment of the present application provides a message assembly method. As shown in FIG. 1, the method includes:

101. Obtain the facial features of the visitor to be authenticated.

Among them, the visitors to be authenticated are outsiders in closed areas such as companies, schools, communities, etc. This embodiment of the application is mainly suitable for authenticating the identities of outsiders in the above closed areas, so as to record the flow of people in the closed areas and ensure that the closed areas are For the personal and property safety of personnel, the executor of the embodiment of this application is a system capable of authenticating the identity of the visitor. For this embodiment of the application, in order to avoid the inconvenience and potential safety hazard caused by manual registration of information to the visitor, the waiting The personal information and characteristic information of the authenticated visitor are entered into the system and stored in the preset feature library, so that when the visitor officially visits, the feature information of the visitor to be authenticated is extracted, and the identity of the visitor to be authenticated is authenticated using the preset feature library.

This application embodiment provides two ways to obtain visitor personal information and characteristic information, namely online and offline. For online methods, visitors can log in to the visitor identity authentication system in advance, register and fill in relevant personal information, including the visitor’s personal information. Personal information such as name, ID number, unit, address, etc., and upload a scanned copy of the ID card online. After the visitor ID authentication system receives the ID card uploaded by the visitor, it uses the preset face detection model to detect and extract the visitor's ID card Further, the preset face recognition model is used to extract the facial features in the visitor’s ID photo, and the visitor’s personal information, visitor photos, and the extracted features of the visitor’s face are correspondingly stored in the preset feature library. Visitors can use the facial features in the preset feature library to authenticate the identity of the visitor when they come officially; for the offline method, when the visitor to be authenticated visits for the first time, he needs to carry his ID card, and the visitor identity authentication system can treat the identity of the authenticated visitor. Identify the front and back of the ID card, extract the personal information in the ID, including name, ID number, address, etc., and use the preset face detection model to detect the photos in the ID card, or directly obtain the identity of the visitor from the electronic chip Furthermore, the preset face recognition model is used to extract the facial features in the visitor’s ID photo, and the extracted visitor’s facial features, visitor personal information and visitor photos are correspondingly stored in the preset feature library, thereby , When the visitor visits again, there is no need to register information or carry an ID card, and the identity of the visitor can be directly authenticated through the preset feature database in the visitor identity authentication system. Among them, for the construction of the preset face recognition model, the photos of all visitors in the preset feature library can be used as training samples, and the training samples are trained to construct the preset face recognition model. At the same time, the embodiment of the present application The blockchain technology is also involved, and the facial features in the preset feature library can be stored in the blockchain.

Further, in order to ensure the accuracy of visitor identity authentication, avoid the inaccurate feature comparison result due to the fact that only a single feature of each visitor is stored in the preset feature library, which in turn affects the accuracy of visitor identity authentication. For this embodiment of the application, It is necessary to collect multiple photos of different forms of different visitors, extract the facial features in each photo, and store multiple facial features corresponding to different visitors in a preset feature library. Specifically, because of the facial detection model obtained The clarity of the ID photo of the visitor is relatively poor. By clearing the ID photo of the visitor, the cleared visitor image can be obtained. Specifically, the preset super-resolution model can be used to clean the ID photo of the visitor. , The preset super-resolution model may be a preset convolutional neural network model, and further, the preset face recognition model is also used to extract facial features in the visitor’s image after the cleaning process, so that the visitor blur can be obtained The facial features of the photo can also obtain the facial features of the visitor’s clear picture. In addition, in order to ensure that multiple features of different visitors are stored in the preset feature library to ensure the accuracy of visitor identity authentication, you can also obtain the visitor ID At the same time of the photo, the visitor is required to upload a recent photo. The recent photo can be one or more recent photos of the visitor without batch processing and light makeup. Furthermore, the facial features of each recent photo uploaded by the visitor are extracted separately. The facial features extracted from different photos are stored in a preset feature library. It should be noted that, in order to ensure the accuracy and effectiveness of the visitor’s identity authentication, the facial features corresponding to each visitor in the preset feature library should not be overridden. For example, at least three facial features of each visitor are stored in the preset feature library.

For the embodiment of this application, when a visitor comes to visit, the camera can be used to collect the visitor's photos, and then the preset face recognition model is used to extract the facial features in the collected photos, so as to compare the facial features of the visitor to be authenticated with the preset feature library The multiple facial features of different visitors are compared, and the identity of the visitor is authenticated. The specific application scenarios of the embodiments of this application can be applied to common interview invitations, business negotiations between different companies, and between different branches and headquarters. Scenes such as business trips and exchanges, registration of migrants from schools and communities, and registration of migrants in closed communities during the epidemic.

102. Perform multi-level analysis according to the facial features of the visitor to be authenticated and multiple facial features corresponding to each visitor in the preset feature library, and determine each target visitor in the preset feature library according to the multi-level analysis result .

Among them, the preset feature library stores multiple facial features corresponding to different visitors. For example, the preset feature library stores five facial features corresponding to visitor A and three facial features corresponding to visitor B. For the implementation of this application, Since the preset feature library stores multiple features of different visitors, when a visitor to be authenticated visits, if the extracted facial features of the visitor to be authenticated are compared with the multiple facial features corresponding to all visitors in the preset feature library, one by one The comparison will increase the workload of comparison and the efficiency of image visitor identity authentication. Therefore, in the embodiment of this application, the extracted facial features of the visitor to be authenticated and the multiple facial features corresponding to different visitors in the preset feature library are multi-level Analyze, exclude most of the visitors in the preset feature database that are not similar to the visitors to be authenticated, retain target visitors that are similar to the visitors to be authenticated, and only compare the facial features corresponding to the visitors to be authenticated with the target visitors in the preset feature library Corresponding multiple facial features are compared one by one, so that while improving the accuracy of visitor identity authentication, it can ensure the authentication efficiency of visitor identity.

Specifically, when comparing the extracted facial features of the visitor to be authenticated with the facial features of different visitors in the preset feature library, the facial features of the visitor to be authenticated are different from multiple facial features corresponding to different visitors. Level analysis and comparison, first calculate the feature centers corresponding to different visitors according to the multiple facial features corresponding to different visitors in the preset feature library, that is, the geometric centers corresponding to multiple facial features of different visitors, and then according to the features corresponding to different visitors The center clusters all visitors in the preset feature library, that is, divides all visitors in the preset feature library into different cluster categories, and selects the target cluster that is closest to the visitor to be authenticated from multiple cluster categories Category, complete the first-level comparative analysis, and further determine the feature center corresponding to each visitor under the target cluster category, and compare the facial features of the visitor to be authenticated with the feature center corresponding to each visitor under the target cluster category. The comparison is performed to obtain the comparison result, and according to the comparison result, the target visitors are selected from each visitor under the target cluster category, thereby completing the second-level comparison analysis, and finally screening out the facial features corresponding to the visitors to be authenticated The multiple facial features corresponding to each target visitor are compared to obtain a comparison result, and the identity of the authenticated visitor is authenticated according to the comparison result, thereby completing the third-level comparative analysis. For the embodiment of this application, by performing multi-level comparative analysis on the facial features of the visitor to be authenticated and multiple facial features corresponding to different visitors in the preset feature library, multiple target visitors in the preset feature library can be screened out and avoid The facial features of the visitors to be authenticated are compared with multiple facial features of different visitors, thereby increasing the workload of comparison and reducing the efficiency of authentication of the visitor's identity.

103. Determine the identity of the visitor to be authenticated according to the multiple facial features corresponding to each target visitor.

For the embodiment of this application, in order to authenticate the identity of the visitor to be authenticated, after multiple target visitors are selected from the preset feature database, the facial features corresponding to the visitor to be authenticated are respectively combined with the multiple faces corresponding to each target visitor. The features are compared, and the identity of the visitor to be authenticated is determined according to the result of the comparison. During the specific comparison, the cosine distance between the facial feature of the visitor to be authenticated and the multiple facial features corresponding to each visitor can be calculated separately, according to the calculated individual The cosine distance is used to determine the identity of the visitor to be authenticated. For example, the facial features corresponding to the visitor to be authenticated are compared with multiple facial features corresponding to the target visitor A. The facial features corresponding to the target visitor A include facial feature 1. Face feature 2 and face feature 3, respectively calculate the cosine distance between the face feature corresponding to the visitor to be authenticated and face feature 1, face feature 2 and face feature 3, which are cosine distance 1, cosine distance 2, and Cosine distance 3, if the cosine distance 1, cosine distance 2 and cosine distance 3 are all greater than or equal to the preset cosine distance, the visitor to be authenticated is regarded as the target visitor A, that is, the visitor to be authenticated is allowed to enter the enclosed area after the identity authentication is passed. The size of the preset cosine distance can be set according to the accuracy requirements of the business party for visitor identity authentication.

The embodiment of this application provides a machine learning-based visitor identity authentication method. Compared with the current method of comparing the extracted visitor features with the single feature of the visitor in the feature database, this application can obtain the facial features of the visitor to be authenticated. And perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, so that according to the multi-level analysis results, the preset feature library and the visitor to be authenticated are excluded The vast majority of visitors who are close to each other only keep a few target visitors who are close to the visitor to be authenticated. Then, the identity of the visitor to be authenticated is determined according to the multiple facial features corresponding to each target visitor, so that the preset can be used The multiple facial features of the target visitor in the feature database are authenticated to the identity of the visitor to be authenticated, which improves the authentication accuracy of the visitor’s identity. At the same time, it can also use multi-level analysis to exclude the facial features of the vast majority of visitors in the preset feature database. It reduces the workload of feature comparison, and while improving the accuracy of visitor identity authentication, it can ensure the efficiency of visitor identity authentication.

Further, in order to better illustrate the above process of authenticating the identity of the visitor, as a refinement and extension of the above embodiment, the embodiment of this application provides another method for authenticating the identity of a visitor based on machine learning, as shown in FIG. 2 As shown, the method includes:

201. Obtain the facial features of the visitor to be authenticated.

For this embodiment of the application, in order to be able to use the preset feature library in the visitor identity authentication system to authenticate the visitor's identity, the ID photo and personal information of the visitor to be authenticated can be obtained online or offline in advance, and use the pre- Set up a face recognition model to extract the facial features in the ID photo, and store the extracted facial features and personal information of the visitors in a preset feature library, which stores all visitors who have visited and will be visiting When a visitor visits formally, the photo of the visitor to be authenticated can be collected through the camera, and then the same preset face recognition model is used to extract the facial features in the photo of the visitor to be authenticated, and the person who is the visitor to be authenticated The facial features are compared with the facial features of different visitors in the preset feature library, and the comparison result is obtained. Based on the comparison result, the identity of the visitor to be authenticated is determined. Further, in order to improve the accuracy of the authentication of the visitor’s identity, it is necessary to increase the preset The feature information in the feature library, specifically, while obtaining the ID photo of the visitor, the visitor can also be required to provide multiple recent photos, and the preset face recognition model can be used to extract the facial features corresponding to each recent photo. At the same time, in order to be able to obtain the facial features corresponding to different photos of visitors, the preset super-resolution model is used to clarify the visitor's ID photo to obtain the cleaned image. The preset super-resolution model can be Preset the convolutional neural network model, and further use the preset face recognition model to extract the facial features of the visitor’s clear picture, so that multiple facial features corresponding to different visitors can be obtained, and the personal information and information of different visitors can be obtained. The photos and multiple facial features are correspondingly stored in the preset feature library, which can enrich the feature information in the preset feature library and improve the accuracy of visitor identity authentication.

202. Perform multi-level analysis according to the facial features of the visitor to be authenticated and multiple facial features corresponding to each visitor in the preset feature library, and determine each target visitor in the preset feature library according to the multi-level analysis result .

For the embodiment of the application, if the extracted facial features of the visitor to be authenticated are compared with multiple facial features corresponding to different visitors in the preset feature library, the comparison workload will be increased, and the efficiency of the image visitor identity authentication will be increased. Therefore, in this embodiment of the application, a multi-level analysis method is used to pre-empt the vast majority of visitors who are not similar to the visitor to be authenticated in the preset feature library, and only multiple target visitors who are similar to the visitor to be authenticated are retained. Comparing the corresponding facial features with the multiple facial features corresponding to each target visitor can not only improve the authentication accuracy of the visitor’s identity, but also ensure the authentication efficiency of the visitor’s identity. Step 202 specifically includes: Multiple facial features, calculate the feature center corresponding to each visitor; based on the calculated feature center corresponding to each visitor, perform cluster analysis on each visitor to obtain multiple cluster categories; according to the person to be authenticated Face features and feature centers corresponding to each visitor under different cluster categories, determine the target cluster category in the multiple cluster categories; according to the facial features of the visitor to be authenticated and each visitor under the target cluster category The corresponding feature center determines each target visitor under the target cluster category.

Further, in order to identify the target cluster category from the multiple cluster categories, the multiple clusters are determined according to the facial features of the visitor to be authenticated and the feature centers corresponding to each visitor in the different cluster categories. The target cluster category in the category category includes: calculating the feature centers corresponding to the different cluster categories according to the feature centers corresponding to each visitor under the different cluster categories; respectively calculating the facial features of the visitors to be authenticated and The first cosine distance between the feature centers corresponding to the different cluster categories, and the target cluster category in the multiple cluster categories is determined based on the calculated first cosine distances. On this basis, in order to determine each target visitor under the target cluster category, the target cluster is determined according to the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor under the target cluster category Each target visitor under the category includes: respectively calculating the second cosine distance between the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor under the target cluster category; and each second cosine distance based on the calculation The chord distance is used to filter out the target visitors from each visitor under the target cluster category. Further, in order to determine each visitor under the target cluster category, the determination of the target cluster category in the plurality of cluster categories based on the calculated first cosine distance includes: The cluster category corresponding to the largest first cosine distance in the chord distance is determined as the target cluster category, and each visitor under the target cluster category is determined. At the same time, based on the calculated second cosine distances, screening the target visitors from each visitor in the target cluster category includes: performing calculations on the target cluster category according to the calculated second cosine distances. Each visitor is sorted, and the target visitors whose sorting rank is within the preset range are filtered out according to the sorting result.

Specifically, first, calculate the feature center corresponding to each visitor according to the multiple facial features corresponding to each visitor in the preset feature library. The specific calculation formula is as follows:

Among them, m is the number of facial features corresponding to a visitor in the preset feature library, (x ₁₁ ,x ₁₂ ,...x _1n ) is the first facial sign corresponding to a visitor, (x _m1 ,x _m2 , …, x _{mn ) is the m-th} face feature corresponding to a visitor, (X ₁ ,X ₂ ,…X n) is the feature center corresponding to a visitor, and the preset feature library can be calculated according to the above formula Feature centers corresponding to different visitors.

Furthermore, according to the characteristic centers corresponding to different visitors, cluster analysis is performed on each visitor to obtain multiple cluster categories. Specifically, the characteristic centers corresponding to each visitor are input into a preset cluster analysis model for cluster analysis, and the result is Multiple cluster categories, each visitor in the preset feature library is divided into cluster categories with different values, where the preset cluster analysis model can be a preset dbscan cluster analysis model, that is, the feature center corresponding to each visitor is input Perform cluster analysis to the preset dbscan cluster analysis model, and set the radius parameter and field density threshold in the clustering process. The radius parameter and field density threshold can be performed according to the number of classifications and the accuracy of the clustering process. By setting, each visitor in the preset feature library can be clustered to obtain multiple cluster categories, and each visitor under different cluster categories can be determined.

Further, in order to filter the target cluster category from multiple cluster categories, that is, to filter out each visitor under the target cluster category that is closest to the visitor to be authenticated from multiple cluster categories, it is necessary to select different cluster categories according to different cluster categories. Download the feature centers corresponding to each visitor, and calculate the feature centers corresponding to different cluster categories.

Among them, m is the number of feature centers corresponding to all visitors under a certain cluster category, (y ₁₁ ,y ₁₂ ,…,y _1n ), (y _m1 ,y _m2 ,…,y _mn ) are respectively a certain cluster The feature center corresponding to the next visitor in the category and the feature center corresponding to the m-th visitor, (Y ₁ , Y ₂ ,..., Y _n ) is the feature center corresponding to a certain clustering category, so according to the above formula according to different clustering The feature centers corresponding to each visitor under the category can be calculated for the feature centers corresponding to different cluster categories.

Further, the first cosine distances between the facial features corresponding to the visitors to be authenticated and the feature centers corresponding to different cluster categories are calculated respectively, and the maximum cosine distances are filtered from the calculated cosine distances, and the maximum cosine distances are corresponded to The cluster category of is determined as the target cluster category, and the specific calculation formula of cosine distance is as follows:

Among them, cosθ is the first cosine distance between the face feature corresponding to the visitor to be authenticated and the feature center corresponding to different cluster categories, (X ₁ ,X ₂ ,...X _n ),(Y ₁ ,Y ₂ ,...Y _n ) are the facial features corresponding to the visitor to be authenticated and the facial features corresponding to different clustering categories, and the above formula can be used to calculate the facial features corresponding to the visitor to be authenticated corresponding to different clustering categories. The first cosine distance between the feature centers of, the larger the first cosine distance cosθ, the closer the visitor to be authenticated is to each visitor in a certain cluster category, so as to filter out the largest first cosine distance, and The corresponding cluster category is used as the target cluster category, and each visitor under other cluster categories that is not similar to the visitor to be authenticated in the preset feature library is excluded, thereby greatly reducing the amount of calculation of feature comparison during visitor identity authentication.

At the same time, in order to further reduce the amount of calculation of feature comparison, continue to filter the target visitors from each visitor under the target cluster category that are more similar to the visitor to be authenticated. For example, the target cluster category includes visitor 1, visitor 2. , Visitor 3, visitor 4, and visitor 5, respectively calculate the second cosine distance between the facial feature corresponding to the visitor to be authenticated and the feature center corresponding to visitor 1, visitor 2, visitor 3, visitor 4, and visitor 5. The calculated size of each second cosine distance sorts each visitor from largest to smallest, and the sorting result is visitor 3, visitor 4, visitor 1, visitor 2, visitor 5, and further, filter the visitors with the top three rankings 3. Visitor 4 and visitor 1 are target visitors that are closer to the visitor to be authenticated. Therefore, only the facial features corresponding to the visitor to be authenticated can be compared with multiple facial features corresponding to the target visitor, which can greatly reduce the features. The amount of comparison calculation improves the efficiency of visitor identity authentication. Wherein, the specific calculation formula of the second cosine distance is the same as the calculation formula of the first cosine distance, and will not be repeated here.

Further, since the different photos uploaded by the visitor correspond to different degrees of importance, that is, the degree to which they can reflect the real characteristics of the visitor is different, therefore, when calculating the feature center corresponding to each visitor in the preset feature library, the multiple facial features are respectively determined A weight value corresponding to each facial feature in, and calculate the feature center corresponding to each visitor based on the determined weight value and the multiple facial features. For example, the clarified ID photo of the visitor can reflect more characteristics of the visitor. Therefore, the weight value corresponding to the facial feature of the clarified ID photo of the visitor is assigned 0.5, and the fuzzy ID photo of the visitor and The weight values corresponding to the facial features of recent selfies are 0.3 and 0.2, respectively.

203. Determine any one of the target visitors, and calculate the third cosine distance between the facial features of the visitors to be authenticated and the facial features corresponding to the any target visitors.

For the embodiment of this application, after determining each target visitor under the target clustering category, it is necessary to compare the facial features corresponding to the visitor to be authenticated and the multiple facial features corresponding to the target visitor one by one to obtain the comparison result, and according to the comparison As a result, the identity of the visitor is authenticated. For example, each target visitor in the preset feature library includes target visitor A and target visitor B, and the facial features corresponding to target visitor A are facial feature 1, facial feature 2, and facial feature 3. The face features corresponding to target visitor B are face feature 4, face feature 5, and face feature 6, respectively. The third is calculated between the face feature corresponding to visitor A to be authenticated and the multiple face features corresponding to target visitor A. The cosine distance is to calculate the third cosine distance between the face feature of the visitor to be authenticated and face feature 1, face feature 2 and face feature 3 respectively, and similarly calculate the face feature corresponding to the visitor to be authenticated The third cosine distance between the multiple facial features corresponding to the target visitor B, that is, the third remainder between the facial features corresponding to the visitor to be authenticated and the facial feature 4, the facial feature 5, and the facial feature 6 are calculated respectively Chord distance.

204. If each calculated third cosine distance is greater than or equal to a preset cosine distance, determine that the visitor to be authenticated is any target visitor.

Following the example above, if the third cosine distance between the face feature of the visitor to be authenticated and the face feature 1, face feature 2 and face feature 3 of the target visitor A are cosine distance 1, cosine distance 2 respectively And cosine distance 3, and cosine distance 1, cosine distance 2, and cosine distance 3 are all greater than or equal to the preset cosine distance, then the visitor to be authenticated is determined to be the target visitor A; if the facial features of the visitor to be authenticated are the same as those of the target visitor B The cosine distances between face feature 4, face feature 5, and face feature 6 are cosine distance 4, cosine distance 5, and cosine distance 6, respectively, and any cosine distance among cosine distance 4, cosine distance 5, and cosine distance 6 is less than With the preset cosine distance, it is determined that the visitor to be authenticated is not the target visitor B. By comparing the facial features corresponding to the visitor to be authenticated with multiple facial features corresponding to the target visitor, the identity of the visitor to be authenticated can be determined according to the comparison result. , At the same time, because multiple facial features of the target visitor are compared with the facial features of the visitor to be authenticated, the authentication accuracy of the visitor's identity is improved. Among them, the preset cosine distance can be set according to the accuracy requirements of the visitor identity authentication. It should be noted that in order to ensure the authentication accuracy of the visitor identity, the preset cosine distance should not be set too small.

Furthermore, due to the limited storage space of the server, and in order to ensure the speed of comparison with the features in the preset feature library during visitor identity authentication, the preset feature library needs to be updated regularly, that is, faces with low accuracy in the preset feature library are deleted. Characteristics. Based on this, the method further includes: determining the number of facial features corresponding to each visitor according to multiple facial features corresponding to each visitor in the preset feature library; and according to the facial features corresponding to each visitor The feature quantity is used to determine the feature visitor to be updated among the visitors; perform cluster analysis on multiple facial features corresponding to the feature visitor to be updated to obtain the feature clustering result corresponding to the feature visitor to be updated; According to the feature clustering result, the feature update is performed on the feature visitor to be updated. Further, the performing feature update on the feature visitor to be updated according to the feature clustering result includes: if the feature clustering result is a plurality of feature categories, determining and deleting the feature visitor corresponding to the feature to be updated Outlier facial features; if the feature clustering result is a single feature category, according to the storage time of multiple facial features corresponding to the feature visitor to be updated, the earliest entry among the multiple facial features is determined and deleted The facial features of the library. In addition, in order to determine the feature visitor to be updated, the determining the feature visitor to be updated in each visitor according to the facial feature quantity corresponding to each visitor includes: determining according to the facial feature quantity corresponding to each visitor The visitor whose number of facial features is greater than the preset number of facial features is determined as the visitor with the feature to be updated. Among them, the preset number of facial features can be determined according to the storage space size of the server and the accuracy requirements for visitor identity authentication.

For example, set the preset number of facial features to 5, and determine that the number of facial features corresponding to visitor A in the preset feature library is 8. Since the number of facial features corresponding to the visitor is greater than the preset number of facial features, visitor A is determined For the feature visitors to be updated, it is necessary to update multiple facial features corresponding to visitor A, and delete the facial features with lower accuracy. Further, cluster analysis of multiple facial features corresponding to visitor A can be used specifically The preset maximum and minimum distance clustering algorithm performs cluster analysis on the multiple facial features corresponding to visitor A, and obtains the feature clustering result corresponding to visitor A, thus by assigning the multiple facial features corresponding to visitor A to the nearest The clustering center divides the multiple facial features corresponding to visitor A into different feature categories. Further, if the feature clustering result is multiple feature categories, for example, one feature category contains 5 facial features, and the other feature category contains 3 facial features, determine the 3 facial features in another feature category as outlier facial features, and delete them. If the feature clustering result is a feature category, then according to the storage time corresponding to each facial feature , Delete the earliest three facial features in the database, thereby realizing the update of the features in the preset feature database.

At the same time, in order to improve the accuracy of the preset face recognition model, the collected photos of the visitors to be authenticated can also be transferred to the visitor identity authentication system and added to the training sample to optimize the preset face recognition model. , The preset face recognition model can be optimized using the photos of the visitors to be authenticated at preset time intervals, and then the optimized preset face recognition model can be used to extract the facial features corresponding to different photos in the preset feature library , Update the facial features in the preset feature library to further improve the accuracy of the facial features in the preset feature library.

In specific application scenarios, each company branch can exchange the visitor's preset feature library, which can facilitate the visit of visitors from different branches. At the same time, sensitive information such as the visitor's ID number is stored in its own database. There will be no information exposure.

The embodiment of this application provides another machine learning-based visitor identity authentication method. Compared with the current method of comparing the extracted visitor features with the single feature of the visitor in the feature database, this application can obtain the face of the visitor to be authenticated. Features; and based on the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library to perform multi-level analysis, so as to exclude the preset feature library and the visitors to be authenticated according to the multi-level analysis results The vast majority of visitors who are not close, only keep a few target visitors close to the visitor to be authenticated, and then determine the identity of the visitor to be authenticated according to the multiple facial features corresponding to each target visitor. Set multiple facial features of the target visitor in the feature library to authenticate the identity of the visitor to be authenticated, which improves the authentication accuracy of the visitor’s identity. At the same time, it can also use multi-level analysis to exclude the facial features of the vast majority of visitors in the preset feature library. It greatly reduces the workload of feature comparison, and while improving the accuracy of visitor identity authentication, it can ensure the efficiency of visitor identity authentication.

Further, as a specific implementation of FIG. 1, an embodiment of the present application provides a visitor identity authentication device based on machine learning. As shown in FIG. 3, the device includes: an acquisition unit 31, an analysis unit 32, and a determination unit 33.

The acquiring unit 31 may be used to acquire the facial features of the visitor to be authenticated. The acquiring unit 31 is the main functional module of the device for acquiring the facial features of the visitor to be authenticated.

The analysis unit 32 may be configured to perform multi-level analysis based on the facial features of the visitor to be authenticated and multiple facial features corresponding to each visitor in the preset feature library, and determine the preset based on the multi-level analysis result Each target visitor in the signature database. The analysis unit 32 performs multi-level analysis in the device according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and determines the preset according to the multi-level analysis result. The main functional modules of each target visitor in the signature database are also core modules.

The determining unit 33 may be configured to determine the identity of the visitor to be authenticated according to multiple facial features corresponding to each target visitor. The determining unit 33 is a main functional module in the device that determines the identity of the visitor to be authenticated according to multiple facial features corresponding to each target visitor, and is also a core module.

For the embodiment of the present application, as shown in FIG. 4, in order to determine each target visitor in the preset feature library according to the multi-level analysis result, the analysis unit 32 includes: a calculation module 321, an analysis module 322, and a determination module 323 .

The calculation module 321 may be used to calculate the feature center corresponding to each visitor according to the multiple facial features corresponding to each visitor.

The analysis module 322 may be used to perform cluster analysis on each visitor based on the calculated feature center corresponding to each visitor to obtain multiple cluster categories.

The determining module 323 may be used to determine the target cluster category among the multiple cluster categories according to the facial features of the visitor to be authenticated and the feature centers corresponding to each visitor in different cluster categories.

The determining module 323 may also be used to determine each target visitor in the target cluster category according to the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor in the target cluster category.

Further, in order to determine the target cluster category among the multiple cluster categories, the determining module 323 includes: a calculation sub-module and a determining sub-module.

The calculation sub-module may be used to calculate the characteristic centers corresponding to the different clustering categories according to the characteristic centers corresponding to each visitor in the different clustering categories.

The determining sub-module may be used to calculate the first cosine distances between the facial features of the visitor to be authenticated and the feature centers corresponding to the different cluster categories, and to calculate the first cosine distances based on the calculated first cosine distances. , Determining the target cluster category among the multiple cluster categories.

Further, the determination module 323 further includes a screening sub-module, and the calculation sub-module may also be used to calculate the facial features of the visitors to be authenticated and the feature centers corresponding to each visitor in the target cluster category. The second cosine distance between.

The screening sub-module may be used to screen out target visitors from each visitor in the target cluster category based on each calculated second cosine distance.

Further, in order to calculate the characteristic center corresponding to each visitor, the calculation module 321 includes a determination sub-module and a calculation sub-module.

The determining sub-module may be used to determine the weight value corresponding to each facial feature of the multiple facial features.

The calculation sub-module may be used to calculate the feature center corresponding to each visitor based on the determined weight value and the multiple facial features.

Further, in order to determine the identity of the visitor to be authenticated, the determining unit 33 includes a calculating module 331 and a determining module 332.

The calculation module 331 may be used to determine any one of the target visitors, and calculate the difference between the facial features of the visitor to be authenticated and the facial features corresponding to any of the target visitors. The third cosine distance.

The determining module 332 may be configured to determine that the visitor to be authenticated is any target visitor if the calculated third cosine distances are all greater than or equal to a preset cosine distance.

Further, in order to update the preset feature library, the device further includes: an update unit 34.

The determining unit 33 may also be configured to determine the number of facial features corresponding to each visitor according to multiple facial features corresponding to each visitor in the preset feature library.

The determining unit 33 may also be configured to determine the feature visitor to be updated among the visitors according to the number of facial features corresponding to each visitor.

The analysis unit 32 may also be used to perform cluster analysis on multiple facial features corresponding to the feature visitor to be updated to obtain a feature clustering result corresponding to the feature visitor to be updated.

The updating unit 34 may be configured to update the characteristics of the visitor with the characteristics to be updated according to the result of the characteristic clustering.

Further, in order to update the feature of the visitor with the feature to be updated, the update unit 34 includes: a first deletion module 341 and a second deletion module 342.

The first deleting module 341 may be used to determine and delete the outlier facial features corresponding to the feature visitor to be updated if the feature clustering result is multiple feature categories.

The second deletion module 342 may be used to determine and delete the multiple face features according to the storage time of the multiple face features corresponding to the feature visitor to be updated if the feature clustering result is a single feature category The face feature that is the earliest in the database.

It should be noted that, for other corresponding descriptions of the functional modules involved in the machine learning-based visitor identity authentication device provided in the embodiment of the present application, reference may be made to the corresponding description of the method shown in FIG. 1, which will not be repeated here.

Based on the above method shown in FIG. 1, correspondingly, an embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the following steps are implemented: Face features; multi-level analysis is performed according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and each of the preset feature libraries is determined according to the multi-level analysis result Target visitor; determine the identity of the visitor to be authenticated according to multiple facial features corresponding to each target visitor.

The computer storage medium may be non-volatile or volatile.

Based on the above-mentioned method shown in FIG. 1 and the embodiment of the apparatus shown in FIG. 3, an embodiment of the present application also provides a physical structure diagram of a computer device. As shown in FIG. 5, the computer device includes: a processor 41, The memory 42 and a computer program that is stored on the memory 42 and can run on the processor, wherein the memory 42 and the processor 41 are both set on the bus 43, and the processor 41 implements the following steps when the processor 41 executes the program: Obtain the pending authentication The facial features of the visitor; perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and determine the preset feature library according to the multi-level analysis result The identity of each target visitor to be authenticated is determined according to multiple facial features corresponding to each target visitor.

Through the technical solution of this application, this application can obtain the facial features of the visitor to be authenticated; and perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library. Therefore, according to the multi-level analysis results, the vast majority of visitors who are not similar to the visitors to be authenticated in the preset feature library are excluded, and only a few target visitors who are similar to the visitors to be authenticated are retained. Then, according to the multiple faces corresponding to each target visitor. Feature to determine the identity of the visitor to be authenticated, so that multiple facial features of the target visitor in the preset feature library can be used to authenticate the identity of the visitor to be authenticated, which improves the authentication accuracy of the visitor’s identity, and can also use multiple levels The analysis excludes the facial features of most visitors in the preset feature library, which greatly reduces the workload of feature comparison. While improving the accuracy of visitor identification, it can ensure the efficiency of visitor identification.

Obviously, those skilled in the art should understand that the above-mentioned modules or steps of this application can be implemented by a general computing device, and they can be concentrated on a single computing device or distributed in a network composed of multiple computing devices. Above, alternatively, they can be implemented with program codes executable by a computing device, so that they can be stored in a storage device for execution by the computing device, and in some cases, they can be executed in a different order than here. Perform the steps shown or described, or fabricate them into individual integrated circuit modules respectively, or fabricate multiple modules or steps of them into a single integrated circuit module for implementation. In this way, this application is not limited to any specific combination of hardware and software.

The above descriptions are only preferred embodiments of the application, and are not intended to limit the application. For those skilled in the art, the application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

Claims (20)

1. A visitor identity authentication method based on machine learning, which includes:

   Obtain the facial features of the visitors to be authenticated;

   Perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and determine each target visitor in the preset feature library according to the multi-level analysis result;

   The identity of the visitor to be authenticated is determined according to the multiple facial features corresponding to each target visitor.
2. The method according to claim 1, wherein the multi-level analysis is performed according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and the determination is made according to the multi-level analysis result Each target visitor in the preset feature library includes:

   Calculating the feature center corresponding to each visitor according to the multiple facial features corresponding to each visitor;

   Based on the calculated feature center corresponding to each visitor, perform a cluster analysis on each visitor to obtain multiple cluster categories;

   Determine the target cluster category among the multiple cluster categories according to the facial features of the visitors to be authenticated and the feature centers corresponding to each visitor in different cluster categories;

   According to the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor under the target cluster category, each target visitor under the target cluster category is determined.
3. The method according to claim 2, wherein said determining the target clusters in the multiple clustering categories based on the facial features of the visitors to be authenticated and the feature centers corresponding to each visitor in the different clustering categories. Class categories, including:

   Calculating the feature centers corresponding to the different cluster categories according to the feature centers corresponding to each visitor under the different cluster categories;

   Calculate the first cosine distances between the facial features of the visitor to be authenticated and the feature centers corresponding to the different cluster categories respectively, and determine the multiple cluster categories based on the calculated first cosine distances Target cluster category in

   The determining each target visitor under the target cluster category according to the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor under the target cluster category includes:

   Respectively calculating the second cosine distance between the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor in the target cluster category;

   Based on the calculated second cosine distances, the target visitors are filtered from the visitors under the target cluster category.
4. The method according to claim 2, wherein the calculating the feature center corresponding to each visitor according to the multiple facial features corresponding to each visitor respectively comprises:

   Respectively determining a weight value corresponding to each of the multiple facial features;

   Based on the determined weight value and the multiple facial features, the feature center corresponding to each visitor is calculated.
5. The method according to claim 1, wherein the determining the identity of the visitor to be authenticated according to the multiple facial features corresponding to the respective target visitors comprises:

   Determine any target visitor among the target visitors, and calculate the third cosine distance between the facial features of the visitor to be authenticated and the facial features corresponding to the any target visitor;

   If each of the calculated third cosine distances is greater than or equal to the preset cosine distance, it is determined that the visitor to be authenticated is any target visitor.
6. The method according to claim 1, wherein the method further comprises:

   Determine the number of facial features corresponding to each visitor according to multiple facial features corresponding to each visitor in the preset feature library;

   Determine the feature visitor to be updated among the visitors according to the number of facial features corresponding to each visitor;

   Performing cluster analysis on multiple facial features corresponding to the feature visitor to be updated to obtain a feature clustering result corresponding to the feature visitor to be updated;

   According to the feature clustering result, perform feature update on the feature visitor to be updated.
7. 8. The method according to claim 6, wherein the performing feature update on the feature visitor to be updated according to the feature clustering result comprises:

   If the feature clustering result is multiple feature categories, determine and delete the outlier facial features corresponding to the feature visitor to be updated;

   If the feature clustering result is a single feature category, according to the storage time of the multiple facial features corresponding to the feature visitor to be updated, the earliest stored facial feature among the multiple facial features is determined and deleted.
8. A visitor identity authentication device based on machine learning, which includes:

   The obtaining unit is used to obtain the facial features of the visitor to be authenticated;

   The analysis unit is configured to perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and to determine the preset feature library according to the multi-level analysis result Each target visitor;

   The determining unit is configured to determine the identity of the visitor to be authenticated according to multiple facial features corresponding to each target visitor.
9. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the following method steps when executed by a processor:

   Obtain the facial features of the visitors to be authenticated;

   Perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and determine each target visitor in the preset feature library according to the multi-level analysis result;

   The identity of the visitor to be authenticated is determined according to the multiple facial features corresponding to each target visitor.
10. The computer-readable storage medium according to claim 9, wherein the multi-level analysis is performed according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and based on the multiple The result of the hierarchical analysis determines each target visitor in the preset feature library, including:

    Calculating the feature center corresponding to each visitor according to the multiple facial features corresponding to each visitor;

    Based on the calculated feature center corresponding to each visitor, perform a cluster analysis on each visitor to obtain multiple cluster categories;

    Determine the target cluster category among the multiple cluster categories according to the facial features of the visitors to be authenticated and the feature centers corresponding to each visitor in different cluster categories;

    According to the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor under the target cluster category, each target visitor under the target cluster category is determined.
11. The computer-readable storage medium according to claim 10, wherein the plurality of cluster categories are determined based on the facial features of the visitor to be authenticated and the feature centers corresponding to each visitor in the different cluster categories The target cluster categories in include:

    Calculating the feature centers corresponding to the different cluster categories according to the feature centers corresponding to each visitor under the different cluster categories;

    Calculate the first cosine distances between the facial features of the visitor to be authenticated and the feature centers corresponding to the different cluster categories respectively, and determine the multiple cluster categories based on the calculated first cosine distances Target cluster category in

    The determining each target visitor under the target cluster category according to the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor under the target cluster category includes:

    Respectively calculating the second cosine distance between the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor in the target cluster category;

    Based on the calculated second cosine distances, the target visitors are filtered from the visitors under the target cluster category.
12. 11. The computer-readable storage medium according to claim 10, wherein the calculating the feature center corresponding to each visitor according to the multiple facial features corresponding to each visitor respectively comprises:

    Respectively determining a weight value corresponding to each of the multiple facial features;

    Based on the determined weight value and the multiple facial features, the feature center corresponding to each visitor is calculated.
13. 9. The computer-readable storage medium according to claim 9, wherein the determining the identity of the visitor to be authenticated according to the multiple facial features corresponding to each of the target visitors respectively comprises:

    Determine any target visitor among the target visitors, and calculate the third cosine distance between the facial features of the visitor to be authenticated and the facial features corresponding to the any target visitor;

    If each of the calculated third cosine distances is greater than or equal to the preset cosine distance, it is determined that the visitor to be authenticated is any target visitor.
14. The computer-readable storage medium of claim 9, wherein the method further comprises:

    Determine the number of facial features corresponding to each visitor according to multiple facial features corresponding to each visitor in the preset feature library;

    Determine the feature visitor to be updated among the visitors according to the number of facial features corresponding to each visitor;

    Performing cluster analysis on multiple facial features corresponding to the feature visitor to be updated to obtain a feature clustering result corresponding to the feature visitor to be updated;

    According to the feature clustering result, perform feature update on the feature visitor to be updated.
15. A computer device includes a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the computer program is executed by the processor to implement the following method steps:

    Obtain the facial features of the visitors to be authenticated;

    Perform multi-level analysis according to the facial features of the visitor to be authenticated and the multiple facial features corresponding to each visitor in the preset feature library, and determine each target visitor in the preset feature library according to the multi-level analysis result;

    The identity of the visitor to be authenticated is determined according to the multiple facial features corresponding to each target visitor.
16. The computer device according to claim 15, wherein the multi-level analysis is performed based on the facial features of the visitor to be authenticated and multiple facial features corresponding to each visitor in the preset feature library, and the multi-level analysis is performed according to the multi-level analysis result Determining each target visitor in the preset feature library includes:

    Calculating the feature center corresponding to each visitor according to the multiple facial features corresponding to each visitor;

    Based on the calculated feature center corresponding to each visitor, perform a cluster analysis on each visitor to obtain multiple cluster categories;

    Determine the target cluster category among the multiple cluster categories according to the facial features of the visitors to be authenticated and the feature centers corresponding to each visitor in different cluster categories;

    According to the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor under the target cluster category, each target visitor under the target cluster category is determined.
17. The computer device according to claim 16, wherein the target in the multiple clustering categories is determined based on the facial features of the visitor to be authenticated and the feature center corresponding to each visitor in the different clustering categories Cluster categories, including:

    Calculating the feature centers corresponding to the different cluster categories according to the feature centers corresponding to each visitor under the different cluster categories;

    Calculate the first cosine distances between the facial features of the visitor to be authenticated and the feature centers corresponding to the different cluster categories respectively, and determine the multiple cluster categories based on the calculated first cosine distances Target cluster category in

    The determining each target visitor under the target cluster category according to the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor under the target cluster category includes:

    Respectively calculating the second cosine distance between the facial feature of the visitor to be authenticated and the feature center corresponding to each visitor in the target cluster category;

    Based on the calculated second cosine distances, the target visitors are filtered from the visitors under the target cluster category.
18. The computer device according to claim 16, wherein the calculating the feature center corresponding to each visitor according to the multiple facial features corresponding to each visitor respectively comprises:

    Respectively determining a weight value corresponding to each of the multiple facial features;

    Based on the determined weight value and the multiple facial features, the feature center corresponding to each visitor is calculated.
19. 15. The computer device according to claim 15, wherein the determining the identity of the visitor to be authenticated according to the multiple facial features corresponding to the respective target visitors comprises:

    Determine any target visitor among the target visitors, and calculate the third cosine distance between the facial features of the visitor to be authenticated and the facial features corresponding to the any target visitor;

    If each of the calculated third cosine distances is greater than or equal to the preset cosine distance, it is determined that the visitor to be authenticated is any target visitor.
20. The computer device according to claim 15, wherein the method further comprises:

    Determine the number of facial features corresponding to each visitor according to multiple facial features corresponding to each visitor in the preset feature library;

    Determine the feature visitor to be updated among the visitors according to the number of facial features corresponding to each visitor;

    Performing cluster analysis on multiple facial features corresponding to the feature visitor to be updated to obtain a feature clustering result corresponding to the feature visitor to be updated;

    According to the feature clustering result, perform feature update on the feature visitor to be updated.

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