WO2020238534A1

WO2020238534A1 - Method and device for data certificate authorization, computer device, and storage medium

Info

Publication number: WO2020238534A1
Application number: PCT/CN2020/087474
Authority: WO
Inventors: 古明涌
Original assignee: 深圳壹账通智能科技有限公司
Priority date: 2019-05-24
Filing date: 2020-04-28
Publication date: 2020-12-03
Also published as: CN110348195A

Abstract

Disclosed are a method and device for data certificate authorization, a computer device, and a storage medium, related to the technical field of artificial intelligence. The method for data certificate authorization comprises: receiving a request of a user for acquiring a data certificate, acquiring a facial image of the user, where the request comprising certificate information of the data certificate that the user intends to acquire; searching, on the basis of the facial image of the user, for acquired certification information associated with facial information of the user; transmitting the acquired certificate information associated with the facial information of the user and the request of the user for acquiring the data certificate to an authorization confirmation server, thus allowing the authorization confirmation server to perform authorization confirmation; receiving an authorization confirmation instruction of the authorization confirmation server, authorizing the data certificate to the user, and updating the acquired certificate information of the user then storing in association with the facial image of the user. As such, the user is prevented from acquiring an excessive number of authorized data certificates.

Description

Data certificate authorization method, device, computer equipment and storage medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on May 24, 2019, the application number is 201910441359.5, and the invention title is "Data certificate authorization method, device, computer equipment and storage medium", the entire content of which is by reference Incorporated in this application.

Technical field

This application relates to the field of artificial intelligence technology, in particular to data credential authorization methods, devices, computer equipment and storage media.

Background technique

In the prior art, for some special data vouchers, without strict identity management, it often happens that some users will obtain too many authorization vouchers, resulting in that the data vouchers cannot really reach the users in need. , Which seriously damages the user’s experience and also greatly wastes computer resources. The inventor realizes that although there are methods for preventing these users from obtaining excessive authorization through identity authentication in the prior art, it does not have a good effect for users with multiple identities.

Summary of the invention

Based on this, in order to solve the technical problem that users obtain too many authorized data vouchers in related technologies and reduce the experience of other users, this application provides a data voucher authorization method, device, computer equipment and storage medium.

In the first aspect, a data certificate authorization method is provided, including:

Receiving a request from a user to obtain a data voucher, obtaining a face image of the user, wherein the request includes voucher information of the data voucher to be obtained by the user;

According to the user’s face image, query the acquired credential information associated with the user’s face image, where the acquired credential information includes the number of data vouchers acquired in the user’s history and the acquired The expiration time of the data certificate;

Send the obtained credential information associated with the user's face information and the user's request for obtaining data credential to the authorization confirmation server, so that the authorization confirmation server is based on the obtained credential information and the user's obtaining data credential Request authorization confirmation;

If the authorization confirmation instruction from the authorization confirmation server is received, the data voucher is authorized to the user, and the acquired credential information of the user is updated and stored in association with the face image of the user.

In a second aspect, a data certificate authorization device is provided, including:

The face image acquisition unit is configured to receive a user's request for acquiring data credentials, and acquire a face image of the user;

The credential information query unit is configured to query the acquired credential information associated with the face information of the user according to the face image of the user, wherein the acquired credential information includes the number of data vouchers that the user has acquired And the expiration time of the obtained data certificate;

The authorization information confirmation unit is configured to send the obtained credential information associated with the user's face information to the authorization confirmation server, so that the authorization confirmation server can perform authorization confirmation;

The data voucher authorization unit is configured to receive an authorization confirmation instruction from the authorization confirmation server, authorize the data voucher to the user, and associate the user's acquired credential information with the user's face image after updating storage.

In a third aspect, a computer device is provided, including a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the above Describe the steps of the data certificate authorization method.

In a fourth aspect, there is provided a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the data voucher authorization method. step.

Due to the uniqueness of the face, users with multiple identities cannot achieve the purpose of obtaining a large number of authorizations by using multiple identities, and can effectively regulate the authorization behavior of the data vouchers. .

Description of the drawings

Fig. 1 is a diagram of an implementation environment of a data credential authorization method provided in an embodiment.

Fig. 2 is a flow chart showing a method for authorizing a data voucher according to an exemplary embodiment.

FIG. 3 is a specific implementation flow chart of step S120 in the data voucher authorization method according to the embodiment corresponding to FIG. 2.

Fig. 4 is another specific implementation flowchart of step S120 in the data voucher authorization method according to the embodiment corresponding to Fig. 2.

Fig. 5 is a specific implementation flow chart of step S130 in the data voucher authorization method according to the embodiment corresponding to Fig. 2.

FIG. 6 is another specific implementation flowchart of step S130 in the data voucher authorization method according to the embodiment corresponding to FIG. 2.

Fig. 7 is a block diagram showing a data voucher authorization device according to an exemplary embodiment.

Fig. 8 schematically shows an exemplary block diagram of an electronic device for implementing the above-mentioned data credential authorization method.

Fig. 9 schematically shows a computer-readable storage medium for implementing the above-mentioned data credential authorization method.

Detailed ways

In order to make the purpose, technical solutions, and advantages of this application clearer, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and are not used to limit the application.

FIG. 1 is an implementation environment diagram of a data credential authorization method provided in an embodiment. As shown in FIG. 1, the implementation environment includes a computer device 100, a user terminal 200, and an authorization confirmation server 300.

The computer device 100 is a data voucher management device, for example, a computer, a server, and other computer devices used by data voucher operation and maintenance personnel. The user terminal 200 is installed with a client that performs a data credential authorization request. After the user submits the data credential authorization through the user terminal 200 running the client, the computer device 100 obtains the user's face image according to the authorization request, and then extracts the user's obtained credential information based on the face image, and then the user's obtained credential information The credential information is sent to the authorization confirmation server 300, and the authorization confirmation server 300 sends the authorization confirmation information to the computer device 100 after performing authorization confirmation according to the obtained credential information and the user's request for obtaining data credential. After receiving the authorization confirmation instruction from the authorization confirmation server, the computer device 100 authorizes the data voucher to the user, and updates the obtained credential information of the user and stores it in association with the face image of the user .

It should be noted that the authorization confirmation server 300, the user terminal 200, and the computer device 100 may be smart phones, tablet computers, notebook computers, desktop computers, etc., but are not limited thereto. The computer device 100, the user terminal 200, and the authorization confirmation server 300 can be connected via Bluetooth, USB (Universal Serial Bus, Universal Serial Bus) or other communication connection methods, which is not limited in this application.

As shown in FIG. 2, in an embodiment, a data voucher authorization method is proposed. The data voucher authorization method can be applied to the above-mentioned computer device 100, and specifically may include the following steps:

Step S110, receiving a request from a user to obtain a data voucher, and obtaining a face image of the user, where the request includes voucher information of the data voucher to be obtained by the user;

After receiving the user's request for obtaining data credentials, first obtain the user's face image, which may be obtained by the camera device of the user terminal 200 or by the camera device of the computer device 100.

For example, in one of the embodiments, the user terminal 200 is the user's mobile phone. After the user sends a request for obtaining data credentials, the user follows the shooting guide displayed on the screen of his mobile phone to take a face image, and then captures the captured face The image is sent to the computer device 100. The shooting guide is to ensure that the image captured by the user is the user's own, rather than randomly shooting other people's faces, and also to reduce the pressure of image processing in the subsequent face recognition process.

In another embodiment, the user requests to obtain data voucher in the voucher granting hall, and the user terminal 200 is a terminal device in the hall for users to operate. When the user sends a request for data voucher, the computer device 100 in the hall will automatically Get the user's face information.

After acquiring the face image of the user, due to the uniqueness of the face, even if the user has multiple identification documents or identity information, but his face only has one, and it cannot be changed in a low-cost manner in a short period of time. It can be used as a basis to confirm whether the user can be authorized.

The request includes the credential information of the data voucher to be obtained by the user, and the credential information of the data voucher to be obtained by the user may include the expiration time of the voucher to be obtained, the characteristic identifier, and the quantity to be obtained by the user. And so on in order to compare in subsequent steps.

Step S120: According to the face image of the user, query the acquired credential information associated with the face image of the user, where the acquired credential information includes the number of data vouchers acquired in the user history and all State the expiration time of the obtained data certificate;

After acquiring the face image of the user, face recognition can be performed, and the acquired credential information associated with the face information of the user can be queried, and the acquired credential information is data that the user has acquired before The voucher information of the voucher, the obtained voucher information may also include the expiration time of the obtained voucher, the characteristic identifier, and the number obtained by the user.

Step S130: Send the obtained credential information associated with the user’s face information and the user’s request for obtaining data credential to the authorization confirmation server, so that the authorization confirmation server is based on the obtained credential information and the user’s acquisition. Data certificate request for authorization confirmation;

After querying the acquired credential information associated with the user's face information, the user's request for data credential acquisition and the acquired credential information associated with the user's face information can be sent to the authorization confirmation server 300 , The authorization confirmation server 300 performs authorization confirmation.

For example, in one embodiment, the authorization confirmation server 300 is a server used by the authorized administrator, and the authorization confirmation server 300 receives the user's request for obtaining data credentials and the information associated with the user's face information. After obtaining the credential information, the authorized administrator confirms the authorization of the request according to the information received by the authorization confirmation server 300. If the data credential that the user has obtained, the expiration time and the data credential to be obtained this time When the number of data vouchers with the same or similar expiration time is greater than the predetermined number, the user can be denied authorization, and if it does not reach the predetermined number, the user can be authorized. The predetermined number may be 1, 2, 5, 9, etc., and is determined according to the purpose and the issued amount of the data voucher, which is not limited in this application.

In another embodiment, after the authorization confirmation server 300 receives the user’s request for data credential acquisition and the acquired credential information associated with the user’s face information, the authorization confirm Request authorization confirmation. The confirmation method is that if the number of data vouchers with the same or similar expiration time as the expiration time of the data vouchers to be obtained this time is greater than a predetermined number among the data vouchers that the user has obtained, the user can be denied authorization , If it does not reach the predetermined number, it will be authorized. The predetermined number may be a fixed value, or may be determined according to a formula, which is not limited in this application. For example, in this embodiment, the data voucher is a data voucher used to obtain a certain limited resource, and the formula for determining the predetermined number may be:

Wherein, n is the predetermined number, W is the pre-issued number of the data vouchers, i is the minimum number of people scheduled to issue the data vouchers, and j is the popularity index of the limited resource, for example, the limited resource is pre-issued The previous pageviews, or the percentage of users who are interested in the limited resources during the survey. The values of W, i, and j are all deployed in the authorization confirmation server 300 before authorization is started.

If the value of n calculated by the above formula is not less than 1, then set the predetermined number to the calculated value of n; if the value of n is less than 1, set the predetermined number to 1.

Step S140: If the authorization confirmation instruction from the authorization confirmation server is received, the data voucher is authorized to the user, and the acquired credential information of the user is updated and stored in association with the face image of the user.

The data voucher can be authorized to the user after authorization confirmation by the authorization confirmation server 300. After authorization, the obtained credential information of the user can be updated, and the updated information can be compared with the user’s personal information. The face image is stored in association for the next use.

For this method, in one of the embodiments of this application, the data voucher is an electronic ticket. In this embodiment, the technical solution of this application can effectively prevent ticket sellers from robbing tickets. Authentication, because in the prior art, most of the methods to prevent ticket sellers from grabbing tickets are through real-name authentication. For this method, ticket sellers can purchase through multiple certificates, and can also obtain a large number of tickets. One method is to limit the number of tickets purchased at a time. For this method, ticket sellers can achieve their goals by purchasing a small number of times.

Therefore, this application uses face recognition, a method of authenticating biological information with a unique identification function, to sell tickets, so as to prevent ticket sellers from selling tickets. The specific principle is that the user’s face The information is bound to its ticket purchase information, and the ticket purchase information includes the number of tickets purchased, and the start time. When a user purchases a ticket, the number of tickets that the user has purchased for the same session as the ticket is displayed. If the user has purchased the number of tickets for the same session or the number of tickets at the same start time plus If the quantity purchased this time is too large, it can be refused to buy too many tickets, thus achieving the effect of preventing reverse buying and selling. Due to the uniqueness of the face of this application, the ticket sellers cannot achieve the purpose of obtaining a large number of tickets by using multiple certificates of different people. This can effectively prevent the ticket sellers from disrupting the market, allowing consumers to obtain tickets fairly, and making consumers The ticketing platform is more trusted.

Optionally, FIG. 3 is a detailed description of step S120 in the data voucher authorization method according to the embodiment corresponding to FIG. 2. In the data voucher authorization method, step S120 may include the following steps:

Step S121, processing the user's face image into a preset size;

Since the device used may be different when acquiring the face image of the user, when recognizing the face image of the user, in order to facilitate the recognition, it is necessary to preprocess the image of the user, namely After the user’s face image is cropped except for the background of the face portion, the cropped image is converted into a predetermined size and predetermined pixels. For example, in one embodiment, the predetermined size is 4.8 cm by 3.3. cm, the predetermined pixel is 600 ppi.

Step S123: Scan the user's face image of a preset size, extract the user's facial features, and form a facial feature vector, where the facial features include the length, width, and length or width of the face , At least one of the length or width of the lips, and the skin color of the face.

In one of the embodiments, the facial features include the length and width of the human face, the distance from the nose to the lips, the distance from the eyes to the nose, the interpupillary distance of the human face, and the skin color of the human face, and the feature vector is composed of

Wherein c, w, l _nl , l _ne , d and r, g, b respectively represent the length and width of the face, the distance from the nose to the lips, the distance from the eyes to the nose, the interpupillary distance of the face, and the skin color of the face.

Step S125: Input the face feature vector of the user into a first machine learning model, and the first machine learning model outputs the identity of the user.

Feature vector

After that, the feature vector

Input the machine learning model, and the machine learning model compares the feature vectors one by one

For each value in the identities of the user, those that are consistent with these characteristic values are found as the identity of the user.

Step S127, according to the user's identity, query the acquired credential information associated with the user's identity.

After the user's identity is obtained, the obtained credential information associated with the user's identity can be queried.

Wherein, the first machine learning model is trained as follows:

Obtain a face image sample set, and each face image sample in the face image sample set has a user identity label attached in advance,

Recognize the facial features of the face from each face image sample in the face image sample set to form a face feature vector;

The face feature vectors of the face image samples are input into the first machine learning model one by one. The first machine learning model outputs the determined user identity and compares it with the posted user identity. If it is inconsistent, adjust the first machine The learning model makes the user identity output by the first machine learning model consistent with the label.

Since the identity tag has been affixed to the sample, the identity of the face image is known. Use the known result as the desired output to train the machine learning model. The learning method is: constantly changing the connection weight of the network under the stimulation of the external input sample. The essence of learning is to dynamically adjust the weight of each connection. Since the expected output is known, if the output of the machine learning model does not match the expected output, the weight of each connection is automatically adjusted until the output obtained is consistent with the expected output. In this way, the first machine learning model is trained. When the first machine learning model is well trained, as long as the feature vectors extracted from the user's face image are input into the first machine learning model in a group, the first machine learning model will be the face image identity of.

Optionally, FIG. 4 is a detailed description of step S120 in the data voucher authorization method according to the embodiment corresponding to FIG. 2. In the data voucher authorization method, step S120 may include the following steps:

Step S122: Scan the face image of the user and the stored face image of the user respectively, and obtain the face features of the face image of the user and the face features of the stored face image of the user.

Wherein, the face image of the user specifically refers to the face image of the user who requests to obtain data credentials. That is, the face image of the user who is currently requesting to obtain data credentials and the face image of the user who have been stored are scanned separately.

In one of the embodiments, the face feature includes the length and width of the face and the length and width of the facial features in the face image.

Step S124: The facial features of the user's facial image and the stored facial features of the user's facial image are input into a second machine learning model together, and the second machine learning model outputs the user's facial image The result is the same as the stored user's face image.

After acquiring the facial features, the machine learning model compares the two sets of facial features one by one, and obtains the difference between each of the two sets of facial features, and then calculates the average of the differences And the variance, and then put it into the formula f(x), the expression of the formula f(x) is

among them,

And S represent the average and variance of the difference, respectively.

Then, integrate f(x), the formula is:

Finally, it is judged whether the obtained value of F exceeds 0.618. If it exceeds 0.618, the machine learning model can judge that the two face images are similar.

Step S126: If the face image of the user is the same as the stored face image of the user, retrieve the acquired credential information associated with the face image.

If it is determined that the face image of the user is the same as the stored face image of the user, the acquired credential information associated with the face image can be retrieved for subsequent processing. If the person of the user is determined If the face image is not the same as the stored all face images, set the acquired credential information associated with the face image to be 0, and proceed to the next step.

Wherein, the second machine learning model is trained as follows:

Using two face photos of the same person taken at different times as a positive sample pair, and two face photos of different people as a negative sample pair, the positive sample pair and the negative sample pair constitute a sample pair set;

Recognize the facial features of the two faces in the two face photos of each sample pair in the sample pair set, respectively, to form a face feature vector;

The face feature vectors of two of each sample pair in the sample pair set are input into the second machine learning model one by one for learning. If a positive sample outputs a different judgment result to the second machine learning model, Or, for negative samples, the same judgment result is output to the second machine learning model, and the second machine learning model is adjusted so that the second machine learning model outputs the opposite judgment result.

Since it is known whether the sample is a positive sample or a negative sample, it is known whether it contains data that conforms to the format rule. Use the known result as the desired output to train the machine learning model. The learning method is: constantly changing the connection weight of the network under the stimulation of the external input sample. The essence of learning is to dynamically adjust the weight of each connection. Since the expected output is known, if the output of the machine learning model does not match the expected output, the weight of each connection is automatically adjusted until the output obtained is consistent with the expected output. In this way, the first machine learning model is trained. When the second machine learning model is well trained, as long as the two sets of facial features extracted from the face photos are input into the second machine learning model, the second machine learning model will output the user’s person The result of whether the face image is the same as the stored user face image.

Optionally, FIG. 5 is a detailed description of step S130 in the data voucher authorization method according to the embodiment corresponding to FIG. 2. In the data voucher authorization method, the obtained voucher information includes information about the data voucher obtained by the user. The quantity and the expiration time of the acquired data voucher, step S130 may include the following steps:

Step S131, judging whether the number of data vouchers with the same valid time as the data vouchers requested by the user that the user has obtained exceeds an alarm threshold according to the acquired credential information associated with the user's face information;

Step S132: If the alarm threshold is exceeded, send the acquired credential information associated with the user's face information, the user's request for data credential acquisition, and the alarm information to the authorization confirmation server, so that the authorization confirmation server refuses to contact the user. The user authorization.

The alarm threshold may be 1, 2, 6, etc., set according to the characteristics of the data voucher, or calculated according to a formula.

In an embodiment of the present application, the formula for determining the warning threshold may be:

Where n is the warning threshold reference value, if n is greater than 1, the warning threshold is set to n, if n is less than 1, the warning threshold is set to 1, and W is the number of pre-issued data vouchers, i is the minimum number of people scheduled to issue the data voucher, and j is the popularity index of the data voucher. The values of W, i, and j are all deployed in the authorization confirmation server 300 before authorization is started.

In an embodiment of the present application, the authorization confirmation server 300 is a server used by the authorized administrator. First, the computer device determines whether the number of data vouchers that have the same validity time as the data vouchers requested by the user exceeds the alarm threshold, and if it exceeds the alarm threshold, sends the acquired information associated with the user's face information The credential information, the user's request for obtaining data credential, and the alarm information are sent to the authorization confirmation server. If the alarm threshold is not exceeded, the alarm information is not sent to the authorization confirmation server 300.

After the authorization confirmation server 300 receives the user's request for obtaining data credentials, the obtained credentials information associated with the user's face information, and the alarm information, the authorized administrator confirms the information received by the server 300 according to the authorization , Carry out a risk assessment, and use the warning information as a basis for denying authorization to the user.

Optionally, FIG. 6 is a detailed description of step S130 in the data voucher authorization method according to the embodiment corresponding to FIG. 2. In the data voucher authorization method, the obtained voucher information includes information about the data voucher obtained by the user. The quantity and the expiration time of the acquired data voucher, step S130 may include the following steps:

Step S131: According to the acquired credential information associated with the face information of the user, determine whether the number of data credential that the user has acquired with the same valid time as the data credential requested by the user exceeds an alarm threshold;

Step S133: If the alarm threshold is exceeded, send the acquired credential information associated with the user's face information, the user's request for data credential acquisition, and the authorization prohibition instruction to the authorization confirmation server to prohibit the authorization confirmation server from pairing The user authorization.

In order to prevent the authorization confirmation server 300 from being cracked and manipulated, in one embodiment of the present application, the computer device 100 first determines whether the number of data vouchers that have been obtained by the user and the valid time of the data vouchers requested by the user exceeds the alarm If the threshold exceeds the alarm threshold, an authorization prohibition instruction is sent to the authorization confirmation server 300 to prohibit the authorization confirmation server 300 from authorizing the user. This ensures that even if the authorization confirmation server is manipulated, the user cannot be authorized.

As with one of the embodiments shown in FIG. 2, in an embodiment of the application, the data voucher is an electronic ticket. In this embodiment, the method of setting a warning threshold is mainly used to increase the matching of ticket sellers. The alarm threshold can be set according to the popularity of the ticket. For hot tickets, a lower alarm threshold can be set, for example, 5, 3, or 2 tickets. For particularly popular tickets, It can even be set to one, and for relatively unpopular tickets, it can be set to a relatively high threshold such as 6, 8, or 9 to achieve the purpose of regulation.

As shown in FIG. 7, in one embodiment, a data voucher authorization device is provided. The data voucher authorization device may be integrated in the above-mentioned computer equipment 100, and may specifically include a face image acquisition unit 110 and a voucher information query unit. 120. Authorization information confirmation unit 130 and data credential authorization unit 140.

The face image obtaining unit 110 is configured to receive a user's request for obtaining data credentials, and obtain a face image of the user;

The credential information query unit 120 is configured to query the acquired credential information associated with the face information of the user according to the face image of the user, wherein the acquired credential information includes the data vouchers that the user has acquired The quantity and the expiration time of the obtained data certificate;

The authorization information confirmation unit 130 is configured to send the obtained credential information associated with the user's face information to the authorization confirmation server, so that the authorization confirmation server can perform authorization confirmation;

The data voucher authorization unit 140 is configured to receive an authorization confirmation instruction from the authorization confirmation server, authorize the data voucher to the user, and update the user's acquired credential information with the user's face image Associated storage.

For the implementation process of the functions and roles of each module in the above-mentioned device, refer to the implementation process of the corresponding steps in the above-mentioned data credential authorization method for details, which will not be repeated here.

It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of a module or unit described above can be further divided into multiple modules or units to be embodied.

In addition, although the various steps of the method of the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be performed in the specific order, or that all the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and/or one step may be decomposed into multiple steps for execution, etc.

Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software, or can be implemented by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (can be a CD-ROM, U disk, mobile hard disk, etc.) or on the network , Including several instructions to make a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

Those skilled in the art can understand that various aspects of the present application can be implemented as a system, method, or program product. Therefore, each aspect of the present application can be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to herein as "Circuit", "Module" or "System".

The electronic device 500 according to this embodiment of the present application will be described below with reference to FIG. 8. The electronic device 500 shown in FIG. 8 is only an example, and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

As shown in FIG. 8, the electronic device 500 is represented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: the aforementioned at least one processing unit 510, the aforementioned at least one storage unit 520, and a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510).

Wherein, the storage unit stores program code, and the program code can be executed by the processing unit 510, so that the processing unit 510 executes the various exemplary methods described in the “exemplary method” section of this specification. Implementation steps. For example, the processing unit 510 may perform step S110 as shown in FIG. 2 to receive a request from a user to obtain a data voucher, and obtain a face image of the user, wherein the request includes the data to be obtained by the user. The credential information of the credential; step S120, query the acquired credential information associated with the user’s face information according to the user’s face image; step S130, send the credential information associated with the user’s face information Obtain credential information and the user's request for obtaining data credentials to the authorization confirmation server, so that the authorization confirmation server performs authorization confirmation according to the obtained credential information and the user's request for obtaining data credentials; step S140, receiving the authorization confirmation server The authorization confirmation instruction is to authorize the data voucher to the user, and update the obtained credential information of the user and store it in association with the face image of the user.

The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 5201 and/or a cache storage unit 5202, and may further include a read-only storage unit (ROM) 5203.

The storage unit 520 may also include a program/utility tool 5204 having a set (at least one) program module 5205. Such program module 5205 includes but is not limited to: an operating system, one or more application programs, other program modules, and program data, Each of these examples or some combination may include the implementation of a network environment.

The bus 530 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any bus structure among multiple bus structures. bus.

The electronic device 500 may also communicate with one or more external devices 700 (such as keyboards, pointing devices, Bluetooth devices, etc.), and may also communicate with one or more devices that enable users to interact with the electronic device 500, and/or communicate with Any device (such as a router, modem, etc.) that enables the electronic device 500 to communicate with one or more other computing devices. This communication can be performed through an input/output (I/O) interface 550. In addition, the electronic device 500 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 560. As shown in the figure, the network adapter 560 communicates with other modules of the electronic device 500 through the bus 530. It should be understood that although not shown in the figure, other hardware and/or software modules can be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.

Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software, or can be implemented by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (can be a CD-ROM, U disk, mobile hard disk, etc.) or on the network , Including several instructions to make a computing device (which may be a personal computer, server, terminal device, or network device, etc.) execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of this specification is stored. The storage medium may be non-volatile or volatile. In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the The terminal device executes the steps according to various exemplary implementations of the present application described in the above "Exemplary Method" section of this specification.

Referring to FIG. 9, a program product 600 for implementing the above method according to an embodiment of the present application is described. It can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be installed in a terminal device, For example, running on a personal computer. However, the program product of this application is not limited to this. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device, or device.

The program product can use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard drives, random access memory (RAM), read-only memory (ROM), erasable Type programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

The computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, and readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than a readable storage medium, and the readable medium may send, propagate, or transmit a program for use by or in combination with the instruction execution system, apparatus, or device.

The program code contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

The program code used to perform the operations of this application can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural programming languages. Programming language-such as "C" language or similar programming language. The program code can be executed entirely on the user's computing device, partly on the user's device, executed as an independent software package, partly on the user's computing device and partly executed on the remote computing device, or entirely on the remote computing device or server Executed on. In the case of a remote computing device, the remote computing device can be connected to a user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using Internet service providers) Business to connect via the Internet).

In addition, the above-mentioned drawings are only schematic illustrations of the processing included in the method according to the exemplary embodiments of the present application, and are not intended for limitation. It is easy to understand that the processing shown in the above drawings does not indicate or limit the time sequence of these processings. In addition, it is easy to understand that these processes can be executed synchronously or asynchronously in multiple modules, for example.

Those skilled in the art will easily think of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. . The description and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

Claims

A data voucher authorization method, wherein the method includes:

Receiving a request from a user to obtain a data voucher, and obtaining a face image of the user, wherein the request includes credential information of the data voucher to be obtained by the user;

According to the user’s face image, query the acquired credential information associated with the user’s face information, where the acquired credential information includes the number of data credential acquired in the user’s history and the acquired credential information. The expiration time of the data certificate;

Send the obtained credential information associated with the user's face information and the user's request for obtaining data credential to the authorization confirmation server, so that the authorization confirmation server is based on the obtained credential information and the user's obtaining data credential Request authorization confirmation;

If the authorization confirmation instruction from the authorization confirmation server is received, the data voucher is authorized to the user, and the acquired credential information of the user is updated and stored in association with the face image of the user.
The method according to claim 1, wherein said querying the acquired credential information associated with the face information of the user according to the face information of the user comprises:

Processing the face image of the user into a preset size;

Scan the user’s face image of a preset size, extract the user’s facial features to form a face feature vector, where the facial features include the length and width of the face, the length or width of the nose, and the length of the lips. At least one of length or width, and skin color of the face;

Inputting the face feature vector of the user into a first machine learning model, and the first machine learning model outputs the identity of the user;

According to the user's identity, query the acquired credential information associated with the user's identity.
3. The method of claim 2, wherein the first machine learning model is trained as follows:

Obtain a face image sample set, each face image sample in the face image sample set is labeled with a user identity label in advance,

Recognize the facial features of the face from each face image sample in the face image sample set to form a face feature vector;

The face feature vectors of the face image samples are input into the first machine learning model one by one. The first machine learning model outputs the determined user identity and compares it with the posted user identity. If it is inconsistent, adjust the first machine The learning model makes the user identity output by the first machine learning model consistent with the label.
The method according to claim 1, wherein said querying the acquired credential information associated with the face information of the user according to the face information of the user comprises:

Respectively scanning the face image of the user and the stored face image of the user, and acquiring the face features of the face image of the user and the face features of the stored face image of the user;

The facial features of the user’s facial image and the stored facial features of the user’s facial image are input into the second machine learning model together, and the second machine learning model outputs the user’s facial image and the stored facial features. Whether the user’s face images are the same;

If the face image of the user is the same as the stored face image of the user, retrieve the acquired credential information associated with the face image.
The method of claim 4, wherein the second machine learning model is trained as follows:

Using two face photos of the same person taken at different times as a positive sample pair, and two face photos of different people as a negative sample pair, the positive sample pair and the negative sample pair constitute a sample pair set;

Recognize the facial features of the faces in the two face photos of each sample pair in the sample pair set respectively, and form the face feature vectors respectively;

The face feature vectors of two of each sample pair in the sample pair set are input into the second machine learning model one by one for learning. If a positive sample outputs a different judgment result to the second machine learning model, Or, for negative samples, the same judgment result is output to the second machine learning model, and the second machine learning model is adjusted so that the second machine learning model outputs the opposite judgment result.
The method of claim 1, wherein the acquired credential information associated with the user’s face information and the user’s request for data credential acquisition are sent to an authorization confirmation server, so that the authorization confirmation server will The credential information and the user’s request for obtaining data credential are authorized to confirm, which specifically includes:

According to the acquired credential information associated with the face information of the user, determine whether the number of data credential that the user has acquired and the valid time of the data credential requested by the user exceeds the alarm threshold;

If the alarm threshold is exceeded, send the acquired credential information associated with the user's face information, the user's data credential acquisition request, and alarm information to the authorization confirmation server, so that the authorization confirmation server refuses to authorize the user Or send the acquired credential information associated with the user's face information, the user's request for data credential acquisition, and the authorization prohibition instruction to the authorization confirmation server to prohibit the authorization confirmation server from authorizing the user.
8. The method of claim 6, wherein the warning threshold is confirmed by the following formula:

Where n is the warning threshold reference value, if n is greater than 1, the warning threshold is set to n, if n is less than 1, the warning threshold is set to 1, and W is the number of pre-issued data vouchers, i is the minimum number of people scheduled to issue the data voucher, and j is the popularity index of the data voucher. The values of W, i, and j are all deployed in the authorization confirmation server before authorization is started.
A data certificate authorization device, wherein the device includes:

A face image acquisition unit, configured to receive a request from a user to acquire a data voucher, and obtain a face image of the user, wherein the request includes credential information of the data voucher to be obtained by the user;

The credential information query unit is configured to query the acquired credential information associated with the face information of the user according to the face image of the user, wherein the acquired credential information includes the number of data vouchers that the user has acquired And the expiration time of the obtained data certificate;

The authorization information confirmation unit is configured to send the obtained credential information associated with the user's face information to the authorization confirmation server, so that the authorization confirmation server can perform authorization confirmation;

The data voucher authorization unit is configured to receive an authorization confirmation instruction from the authorization confirmation server, authorize the data voucher to the user, and associate the user's acquired credential information with the user's face image after updating storage.
A computer device includes a memory and a processor, and computer-readable instructions are stored in the memory. When the computer-readable instructions are executed by the processor, the processor executes the following steps:

Receiving a request from a user to obtain a data voucher, and obtaining a face image of the user, wherein the request includes credential information of the data voucher to be obtained by the user;

According to the face image of the user, query the acquired credential information associated with the face information of the user, where the acquired credential information includes the number of data vouchers that have been acquired in the user history and the acquired credential information The expiration time of the data certificate;

Send the obtained credential information associated with the user's face information and the user's request for obtaining data credential to the authorization confirmation server, so that the authorization confirmation server is based on the obtained credential information and the user's obtaining data credential Request authorization confirmation;

If the authorization confirmation instruction from the authorization confirmation server is received, the data voucher is authorized to the user, and the acquired credential information of the user is updated and stored in association with the face image of the user. 10. The computer device of claim 9, wherein, when the computer-readable instructions are executed by the processor, the processor executes the query based on the user's face information and the user The steps of obtaining the credential information associated with the facial information include:

Processing the face image of the user into a preset size;

Scan the user’s face image of a preset size, extract the user’s facial features to form a face feature vector, where the facial features include the length and width of the face, the length or width of the nose, and the length of the lips. At least one of length or width, and skin color of the face;

Inputting the face feature vector of the user into a first machine learning model, and the first machine learning model outputs the identity of the user;

According to the user's identity, query the acquired credential information associated with the user's identity.
The computer device according to claim 10, wherein, when the computer-readable instructions are executed by the processor, the processor executes the input of the user's face feature vector into the first machine learning model Before the step, it is also used to perform the following steps:

Obtain a face image sample set, each face image sample in the face image sample set is labeled with a user identity label in advance,

Recognize the facial features of the face from each face image sample in the face image sample set to form a face feature vector;

The face feature vectors of the face image samples are input into the first machine learning model one by one. The first machine learning model outputs the determined user identity and compares it with the posted user identity. If it is inconsistent, adjust the first machine The learning model makes the user identity output by the first machine learning model consistent with the label.
The computer device according to claim 9, wherein, when the computer-readable instructions are executed by the processor, the processor executes the query based on the face information of the user, The steps of obtaining credential information associated with face information include:

Respectively scanning the face image of the user and the stored face image of the user, and acquiring the face features of the face image of the user and the face features of the stored face image of the user;

The facial features of the user’s facial image and the stored facial features of the user’s facial image are input into the second machine learning model together, and the second machine learning model outputs the user’s facial image and the stored facial features. Whether the user’s face images are the same;

If the face image of the user is the same as the stored face image of the user, retrieve the acquired credential information associated with the face image.
The computer device according to claim 9, wherein, when the computer-readable instructions are executed by the processor, the processor executes the combination of the facial features of the user’s facial image with the stored Before the step of inputting the face features of the user's face image into the second machine learning model, it is also used to perform the following steps:

Using two face photos of the same person taken at different times as a positive sample pair, and two face photos of different people as a negative sample pair, the positive sample pair and the negative sample pair constitute a sample pair set;

Recognize the facial features of the two faces in the two face photos of each sample pair in the sample pair set, respectively, to form a face feature vector;

The face feature vectors of two of each sample pair in the sample pair set are input into the second machine learning model one by one for learning. If the second machine learning model outputs different judgment results for the positive samples, Or, for negative samples, the same judgment result is output to the second machine learning model, and the second machine learning model is adjusted so that the second machine learning model outputs the opposite judgment result.
The computer device according to claim 9, wherein, when the computer-readable instructions are executed by the processor, the processor executes the sending of the acquired credential associated with the face information of the user The information and the user's request for obtaining data credentials are sent to the authorization confirmation server, so that the authorization confirmation server performs authorization confirmation according to the obtained credential information and the user's request for obtaining data credentials, including:

According to the acquired credential information associated with the face information of the user, judging whether the number of data credential that the user has acquired with the same valid time as the data credential requested by the user exceeds an alarm threshold;

If the alarm threshold is exceeded, send the acquired credential information associated with the user's face information, the user's data credential acquisition request, and alarm information to the authorization confirmation server, so that the authorization confirmation server refuses to authorize the user Or send the acquired credential information associated with the user's face information, the user's request for data credential acquisition, and the authorization prohibition instruction to the authorization confirmation server to prohibit the authorization confirmation server from authorizing the user.
A storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the following steps:

Receiving a request from a user to obtain a data voucher, and obtaining a face image of the user, wherein the request includes credential information of the data voucher to be obtained by the user;

According to the user’s face image, query the acquired credential information associated with the user’s face information, where the acquired credential information includes the number of data credential acquired in the user’s history and the acquired credential information. The expiration time of the data certificate;

Send the obtained credential information associated with the user's face information and the user's request for obtaining data credential to the authorization confirmation server, so that the authorization confirmation server is based on the obtained credential information and the user's obtaining data credential Request authorization confirmation;

If the authorization confirmation instruction from the authorization confirmation server is received, the data voucher is authorized to the user, and the acquired credential information of the user is updated and stored in association with the face image of the user.
The storage medium according to claim 15, wherein, when the computer-readable instructions are executed by one or more processors, the one or more processors execute the query and query based on the facial information of the user. The step of obtaining the credential information associated with the user's face information includes: processing the user's face image into a preset size;

Scan the user’s face image of a preset size, extract the user’s facial features to form a face feature vector, where the facial features include the length and width of the face, the length or width of the nose, and the length of the lips. At least one of length or width, and skin color of the face;

Inputting the face feature vector of the user into a first machine learning model, and the first machine learning model outputs the identity of the user;

According to the user's identity, query the acquired credential information associated with the user's identity.
The storage medium of claim 16, wherein, when the computer-readable instructions are executed by one or more processors, one or more processors execute the input of the user’s facial feature vector into the first Before the steps of the machine learning model, it is also used to perform the following steps:

Obtain a face image sample set, and each face image sample in the face image sample set has a user identity label attached in advance,

Identify the facial features of the face from each face image sample in the face image sample set to form a face feature vector;

The face feature vectors of the face image samples are input into the first machine learning model one by one. The first machine learning model outputs the determined user identity and compares it with the posted user identity. If it is inconsistent, adjust the first machine The learning model makes the user identity output by the first machine learning model consistent with the label.
The storage medium according to claim 15, wherein, when the computer-readable instructions are executed by one or more processors, the one or more processors execute the query and query based on the facial information of the user. The steps of obtaining credential information associated with the user's face information include:

Respectively scanning the face image of the user and the stored face image of the user, and acquiring the face features of the face image of the user and the face features of the stored face image of the user;

The facial features of the user’s facial image and the stored facial features of the user’s facial image are input into the second machine learning model together, and the second machine learning model outputs the user’s facial image and the stored facial features. Whether the user’s face images are the same;

If the face image of the user is the same as the stored face image of the user, retrieve the acquired credential information associated with the face image.
The storage medium of claim 18, wherein, when the computer-readable instructions are executed by one or more processors, the one or more processors execute the conversion of the facial features of the user's facial image Before the step of inputting the second machine learning model together with the stored facial features of the user's face image, it is also used to perform the following steps:

Using two face photos of the same person taken at different times as a positive sample pair, and two face photos of different people as a negative sample pair, and the positive sample pair and the negative sample pair constitute a sample pair set;

Recognize the facial features of the faces in the two face photos of each sample pair in the sample pair set respectively, and form the face feature vectors respectively;

The face feature vectors of two of each sample pair in the sample pair set are input into the second machine learning model one by one for learning. If a positive sample outputs a different judgment result to the second machine learning model, Or, for negative samples, the same judgment result is output to the second machine learning model, and the second machine learning model is adjusted so that the second machine learning model outputs the opposite judgment result.
The storage medium according to claim 15, wherein, when the computer-readable instructions are executed by one or more processors, one or more processors execute the sending of the information associated with the user's face The obtained credential information and the user's request for obtaining data credential are sent to the authorization confirmation server, so that the authorization confirmation server performs authorization confirmation according to the obtained credential information and the user's request for obtaining data credential, including:

According to the acquired credential information associated with the face information of the user, determine whether the number of data credential that the user has acquired and the valid time of the data credential requested by the user exceeds the alarm threshold;

If the alarm threshold is exceeded, send the acquired credential information associated with the user's face information, the user's request for data credential acquisition, and alarm information to the authorization confirmation server, so that the authorization confirmation server refuses to authorize the user Or send the acquired credential information associated with the face information of the user, the user's request for data credential acquisition, and the authorization prohibition instruction to the authorization confirmation server to prohibit the authorization confirmation server from authorizing the user.