CN113961899A - Real-name authentication method, device, system, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure provides a real-name authentication method, a real-name authentication device, a real-name authentication system, electronic equipment and a storage medium, and relates to the field of artificial intelligence, in particular to the field of computer vision and deep learning. The specific implementation scheme is as follows: after the face image data is collected, a plurality of function modules which are configured aiming at the current application scene and need to be called and a calling sequence are obtained by reading configuration information, and after the face information is detected, one or more face living body detection modules comprising a silence living body detection module, an action living body detection module and a dazzling pupil living body detection module in the configuration information are called in sequence according to the calling sequence to carry out face living body detection; and after the human face living body detection is passed, the real-name authentication is completed through a real-name authentication server at the cloud end. By applying the real-name authentication method provided by the disclosure, different function modules and calling sequences can be configured aiming at different application scenes, so that the method can adapt to different service application scenes and has high universality.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and in particular to computer vision and deep learning techniques.
Background
At present, many network service application scenarios involve some private data, and therefore, for such network service application scenarios, real-name authentication needs to be performed on a user.
Disclosure of Invention
The present disclosure provides a real-name authentication method, apparatus, system, electronic device, and storage medium capable of adapting to different service application scenarios.
According to an aspect of the present disclosure, there is provided a real-name authentication method including:
collecting face image data;
reading configuration information, and obtaining a plurality of function modules which are configured aiming at the current application scene and need to be called and a calling sequence; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a flare pupil liveness detection module;
according to the calling sequence, calling a face detection module to carry out face detection on the collected face image data;
after the face information is detected, one or more face living body detection modules contained in the configuration information are sequentially called according to the calling sequence to carry out face living body detection;
after the human face living body detection is passed, the human face image data is sent to a cloud real-name authentication server for real-name authentication, and an authentication result returned by the cloud real-name authentication server is obtained.
According to another aspect of the present disclosure, there is provided a real-name authentication apparatus including:
the face image acquisition unit is used for acquiring face image data;
the device comprises a configuration information reading unit, a configuration information processing unit and a configuration information processing unit, wherein the configuration information reading unit is used for reading configuration information and obtaining a plurality of function modules which are configured aiming at the current application scene and need to be called and a calling sequence; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a flare pupil liveness detection module;
the face detection unit is used for calling the face detection module to carry out face detection on the collected face image data according to the calling sequence;
the face living body detection unit is used for sequentially calling one or more face living body detection modules contained in the configuration information according to the calling sequence after the face information is detected so as to carry out face living body detection;
and the real-name authentication unit is used for sending the face image data to a real-name authentication server at the cloud end for real-name authentication after the face living body detection is passed, so as to obtain an authentication result returned by the real-name authentication server at the cloud end.
According to another aspect of the present disclosure, there is provided a real-name authentication system including: a client and a cloud real-name authentication server;
the client is used for acquiring face image data; reading configuration information, and obtaining a plurality of function modules which are configured aiming at the current application scene and need to be called and a calling sequence; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a flare pupil liveness detection module; according to the calling sequence, calling a face detection module to carry out face detection on the collected face image data; after the face information is detected, one or more face living body detection modules contained in the configuration information are sequentially called according to the calling sequence to carry out face living body detection; after the living human face detection is passed, sending the human face image data to a cloud real-name authentication server;
and the cloud real-name authentication server is used for performing real-name authentication on the face image data and returning an authentication result to the client.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the real-name authentication methods described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform any of the real-name authentication methods described above.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements any of the real-name authentication methods described above.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic diagram of a first embodiment of a real name authentication method provided in accordance with the present disclosure;
FIG. 2 is a diagram of an example of functional module configurations for different application scenarios in accordance with the real-name authentication method provided by the present disclosure;
FIG. 3a is a schematic diagram illustrating a function module calling sequence in a second embodiment of the real-name authentication method provided by the present disclosure;
fig. 3b is a schematic flow chart of face live body detection in a second embodiment of the real-name authentication method provided by the present disclosure;
fig. 3c is a schematic diagram illustrating the principle of face detection in the second embodiment of the real-name authentication method provided by the present disclosure;
FIG. 4 is a schematic diagram illustrating a function module calling sequence in a third embodiment of a real-name authentication method provided by the present disclosure;
FIG. 5 is a schematic diagram illustrating a function module calling sequence in a fourth embodiment of a real-name authentication method according to the present disclosure;
FIG. 6 is a timing diagram of a fifth embodiment of a real name authentication method provided in accordance with the present disclosure;
fig. 7 is a schematic structural diagram of a first embodiment of a real-name authentication device provided according to the present disclosure;
fig. 8 is a schematic structural diagram of a second embodiment of a real-name authentication device provided according to the present disclosure;
fig. 9 is a schematic structural diagram of a first embodiment of a real-name authentication system provided according to the present disclosure;
fig. 10 is a schematic structural diagram of a second embodiment of a real-name authentication system provided according to the present disclosure;
fig. 11 is a block diagram of an electronic device for implementing a real-name authentication method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The present disclosure provides a real-name authentication method, apparatus, system, electronic device, and storage medium capable of adapting to different service application scenarios.
First, terms of technical expertise related to the present disclosure are explained:
silent live body detection: the living body detection method is used for detecting whether the human face is a real living body human face or not based on the human face picture under the condition of no action coordination.
And (3) action living body detection: the method is a living body detection mode for detecting whether a real living body face exists or not based on a video image through combined actions of blinking, mouth opening, head shaking, head nodding and the like.
And (3) detecting a dazzling pupil living body: the living body detection method is used for detecting whether the human face is a real living body face or not based on the reflection performance of the face and the pupil in the human face picture.
Next, a real-name authentication method, device, system, electronic device, and storage medium provided by the present disclosure will be described in detail.
Referring to fig. 1, the real-name authentication method provided by the present disclosure may include the following steps:
step S110, collecting face image data.
Step S120, reading configuration information, and obtaining a plurality of function modules which need to be called and are configured for the current application scene and a calling sequence; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a glare pupil liveness detection module.
In this embodiment, the configuration information may be configured by a user through a preset configuration interface. Specifically, after receiving a configuration request from a user, a configuration interface may be displayed, where each selectable function module may be displayed in the configuration interface; then, receiving a functional module selected by a user in a configuration interface aiming at the current application scene; and generating an execution sequence of each function module selected by the user based on a preset logic sequence. Therefore, a plurality of function modules which need to be called and are configured according to the current application scene and the calling sequence are obtained and are stored as configuration information.
And step S130, calling a face detection module to perform face detection on the acquired face image data according to the calling sequence.
And step S140, after the face information is detected, sequentially calling one or more face living body detection modules contained in the configuration information according to the calling sequence to perform face living body detection.
And S150, after the living human face detection is passed, sending the human face image data to a cloud real-name authentication server for real-name authentication, and obtaining an authentication result returned by the cloud real-name authentication server.
As can be seen from the foregoing embodiments, in the real-name authentication process, after face image data is collected, a plurality of function modules and a calling sequence that need to be called and are configured for a current application scene are obtained by reading configuration information, and after face information is detected according to the calling sequence, one or more face living body detection modules including a silence living body detection module, an action living body detection module and a pupil living body detection module in configuration information are sequentially called according to the calling sequence to perform face living body detection; and after the human face living body detection is passed, the real-name authentication is completed through a real-name authentication server at the cloud end.
It can be seen that, by applying the real-name authentication method provided by the present disclosure, a plurality of different function modules and calling sequences can be configured for different application scenarios, and particularly for living human face detection, different living human face detection modules including one or more of a silent living human face detection module, an action living human face detection module, and a pupil living human face detection module can be configured for different application scenarios, so that the method can adapt to different service application scenarios, and has high universality. Meanwhile, compared with the mode that different real-name authentication codes need to be developed in different service application scenes in the prior art, the development and maintenance cost is reduced.
In other embodiments, in order to improve the accuracy of the face live detection, the various functional modules may further include: and a human face quality detection module. Before the living human face detection, a human face quality detection module is called to carry out quality detection on human face information; and after the quality detection is passed, carrying out human face living body detection. Thus, the accuracy of the human face living body detection can be improved.
In addition, in order to adapt to different application scenarios, the number and the types of actions of the action living body detection module can be configured; similarly, the detection strategy of the glare pupil live detection module can also be set to be a preset strict or normal strategy according to the needs of the application scenario. Therefore, the configuration of the real-name authentication method is more flexible, and the method can be more suitable for different application scenes.
Specifically, referring to fig. 2, fig. 2 shows functional modules that can be called by three application scenarios.
The logistics traffic scene can be configured into a traffic edition, and the called function module can include: the device comprises a face detection module, a face quality detection module, a silence living body detection module and an action living body detection module.
For the logistics traffic scene, in order to improve the accuracy of human face live body detection, the configuration information of the number and types of actions of the action live body detection module is configured for the live body detection of complex actions, that is, the live body detection including more than two actions, for example: comprises the living body detection of the eyes opening and closing, the mouth opening and closing, the head swinging left and right and the head raising up and down.
For a financial insurance scenario, configurable to a financial edition, the invoked functional module may include: the system comprises a face detection module, a face quality detection module, an action living body detection module and a dazzling pupil living body detection module.
For the financial insurance scenario, the user may include a group of elderly people, which are often relatively low in fitness and high in security requirement. Therefore, the motion live body detection is configured to be two simple motions of opening and closing the eyes and opening and closing the mouth, and the living body detection module for the flare pupil is configured to be called up, and further, the living body detection of the human face is performed. Meanwhile, the detection strategy of the glare pupil live detection module may be configured as follows: and a preset strict detection strategy is adopted to further improve the accuracy of the human face living body detection.
For a social interaction entertainment scene, the social interaction entertainment scene can be configured into a social interaction entertainment version, and the called function module can comprise: the device comprises a face detection module, a face quality detection module and a silence living body detection module.
For the social interaction entertainment scene, the safety requirement is relatively low, so the action living body detection can be configured to be only silent living body detection, and the action living body detection and the dazzling pupil living body detection are not required.
In addition, the silent liveness detection can be realized by a pre-trained silent liveness detection model. For a social interaction entertainment scenario, for example: in a live broadcast scene, many face images are face images processed by facial beautification. In order to effectively perform silent living body detection on a face image subjected to face beautification processing, before a silent living body detection model is trained, original image data is subjected to face beautification, age reduction and other transformations when a sample is collected, and the transformed image is used as a training sample, so that the silent living body detection model learns the features of the face beautification, age reduction and other transformations, and the accuracy of the face living body detection is further improved.
In addition, in scenes such as game APP, real-name authentication can be performed during login of teenagers/real-name authentication in the game process, the fact that the real person is verified, the real-name authentication function is completed, the Internet surfing duration of the teenagers is monitored, and the game/live account is closed within a set time, so that the purpose of preventing addiction is achieved.
Referring to fig. 3a, a call sequence of each function module in a logistics traffic scenario in a second embodiment of the real-name authentication method provided by the present disclosure is shown. As shown in fig. 3a, the calling sequence of each function module in the logistics traffic scene is as follows: the face detection module 310, then the face quality detection module 320, then the silence liveness detection module 330, and finally the action liveness detection module 340 are invoked.
Referring to fig. 3b, a schematic flow chart of face live detection in a logistics traffic scene is shown. As shown in fig. 3b, the process includes the following steps:
step S310, a face detection module is called to detect the face.
In this step, the processing of the face detection module mainly includes: and inputting the collected face image into a pre-trained face detection model to obtain a face living body score output by the face detection model.
Specifically, the detection can be performed by using a trained detect model or align model.
For video streaming, continuous human face picture stream frames exist, and the continuous human face picture stream frames directly enter an alignment model to perform key point prediction, namely tracking (track). As shown in fig. 3c, after the face is detected for the first time, the key point detection and positioning are performed, and the key point alignment model is entered, the key point position is initialized at the next frame, the position of the next step is predicted according to the position of the previous step and the graphic features, and the iteration is continued for the next round until the key points are matched. And if the track fails, restarting to detect the face for the first time.
Step S320, a face quality detection module is called to perform quality detection on the face information.
In this step, the collected video stream can be input into a pre-trained quality detection model for quality detection, and the quality detection model outputs a face picture meeting the quality detection requirement.
And step S330, after the quality detection is passed, calling a silent living body detection module to perform silent living body detection.
In this step, the face picture meeting the quality detection requirement can be input into a pre-trained silent living body detection model, and the probability that the face is a living body, that is, the face living body score, is obtained. And determining to pass the silent live body detection under the condition that the score exceeds a preset threshold value according to the face live body score.
In this embodiment, the silent live body detection can adopt a model for carrying out special training optimization on human face image samples in a complex scene simulating a real person, such as face scratching by a photo, simulation of the real person outline by the hand-held photo curvature, bending of the upper half body curvature of the photo and the like, so that a more complex attack mode can be identified on the basis of identifying common resistance to the copying of the picture, the photo and the screen by the live body detection model, and the attack prevention level of the model is integrally improved. Specifically, RGB silent liveness detection may be employed.
In step S340, after the silent biopsy passes, an action biopsy is performed.
In this embodiment, the motion living body detection can be performed for several motions of opening and closing the eyes, opening and closing the mouth, shaking the head left and right, and nodding the head up and down. In other embodiments, different configurations may be implemented according to different scene needs.
In step S350, after the action live body detection is passed, the face image may be encrypted for subsequent real-name authentication.
Therefore, the embodiment can obtain a more accurate living body detection result aiming at a traffic logistics scene.
Referring to fig. 4, a call sequence of each function module in a financial insurance scenario in a third embodiment of the real-name authentication method provided according to the present disclosure is shown. As shown in fig. 4, the calling sequence of each functional module in the financial insurance scenario is: the face detection module 310, the face quality detection module 320, the action live detection module 340, and the flare pupil live detection module 440 are invoked first.
In this embodiment, based on the screen on the color light reaction is being played to the face, the different facial reflection of light principle of expression after the processing and the reflection of light expression of paper, screen, the main process of dazzling pupil live body detection module carrying out live body detection can include: the face is randomly polished based on a mobile phone screen, and whether the face is a living body or not is judged through the reflection performance of the face and the pupil.
Specifically, the current face image acquired by the mobile phone can be input into a pre-trained pupil live detection model for pupil live detection.
In addition, in the embodiment, pupil light reflection treatment can be added on the basis of face light reflection, so that the attacking effect of a human-like skin mask and the like is enhanced. The overall attack rejection rate is further improved.
Therefore, the embodiment can obtain more accurate living body detection results for the financial insurance scene.
Referring to fig. 5, a call sequence of each function module in a social interaction entertainment scenario in a fourth embodiment of the real-name authentication method provided by the present disclosure is shown. As shown in fig. 5, the calling sequence of each function module in the social entertainment scene is: the face detection module 310 is invoked first, then the face quality detection module 320, and finally the silence liveness detection module 330.
Because the social interaction entertainment scene usually has low requirements on safety, the action living body detection module and the dazzling pupil living body detection module do not need to be called, and the human face living body detection steps are further simplified on the premise of adapting to the social interaction entertainment scene.
In other embodiments, before the real-name authentication is performed by the cloud real-name authentication server, the device risk control and the data risk control may be performed by the cloud big-data risk control server.
Specifically, after the human face living body detection is passed, the step of sending the human face image data to the real-name authentication server at the cloud for real-name authentication to obtain an authentication result returned by the real-name authentication server at the cloud may include:
calculating the face image data based on a preset safety algorithm to obtain safety detection data;
sending the encrypted face image data and the encrypted safety detection data to a cloud real-name authentication server so that the cloud real-name authentication server sends the face image data and the safety detection data to a big-data risk control server, and performing real-name authentication on the face image data under the condition that a data pneumatic control result returned by the big-data risk control server shows that the face image data and the safety detection data have no risk; and obtaining an authentication result returned by the cloud real-name authentication server.
Therefore, the human face image data subjected to real-name authentication has no safety risk, and the safety of the real-name authentication is improved.
In addition, the risk control of the device can be performed before the face image data is acquired. Specifically, before the face image data is collected, risk scanning can be performed on the equipment to obtain equipment risk scanning data; then, the equipment risk scanning data are sent to a cloud big data risk control server, and an equipment wind control result returned by the big data risk control server is obtained; and then, under the condition that the equipment wind control result shows that the equipment has no risk, executing the step of acquiring the facial image data.
Therefore, the electronic equipment for real-name authentication, such as a mobile phone and the like, can be ensured to have no safety risk, and the safety of the real-name authentication is further improved.
Referring to fig. 6, the timing sequence of the fifth embodiment of the real name authentication method provided according to the present disclosure may include:
1. the user opens the face authentication function at the client, and the client displays a face verification interface.
2. And the client starts to carry out equipment risk detection and carries out risk scanning on the equipment to obtain equipment risk scanning data.
3. And the client sends the equipment risk scanning data to a big data risk control server at the cloud end to carry out big data wind control.
4. And after the big data risk control server carries out equipment risk control, returning an equipment wind control result to the client.
5. If the device wind control result indicates that the wind control is not hit, namely the device has no risk, the client executes the step 6; and if the device risk control result indicates that the risk control is hit, namely the device is risky, the client executes the steps-5 and-6, and outputs the prompt of the risk of the device to the user through the face verification interface.
In this embodiment, the device risk scanning data may include an IP address, a device state, account information, an operation behavior of a device, and the like of an electronic device where the client is located, such as a mobile phone and other terminal devices.
Thus, the big data wind control server can carry out risk IP identification (such as IDC IP, second broadcast IP, proxy IP and the like), abnormal detection (such as abnormal equipment detection, abnormal account detection and abnormal behavior detection) and the like; the big data wind control server can obtain a wind control label based on the processing and judgment of the real-time wind control engine and the offline strategy model, wherein the wind control label is high-risk/medium-risk/low-risk/risk-free; and if the wind control result is high risk, namely the wind control is hit, terminating the subsequent real-name authentication process from the link of equipment wind control scanning, and if the wind control result is not high risk, returning no result (considering the equipment wind control PASS) and continuing the subsequent process.
6. Before the client camera carries out data acquisition, the safety detection of the camera can be carried out firstly.
7. Under the condition that the camera is safe, the client transmits original face image data acquired by the camera into the living body detection offline model.
8. The living body detection offline model can firstly carry out image quality detection to obtain a human face image meeting the quality requirement. And if the image quality is not qualified, circularly executing the steps 6-8.
9. And the living body detection offline model performs living body detection on the face image data with qualified image quality and returns the face image data with qualified living body detection.
The offline living body detection model in the embodiment may include, according to the configuration: the system comprises a face detection module and a face quality detection module; and one or more of a silence liveness detection module, an action liveness detection module, and a glare pupil liveness detection module.
10. The client calls a preset safety API, generates safety detection data based on the face image data, encrypts the face image data and the safety detection data, and sends the encrypted face image data and the encrypted safety detection data to a cloud real-name authentication server.
11. And the real-name authentication server executes the face cloud service to decrypt the data.
12. And the real-name authentication server sends the face image data and the safety detection data to the big data risk control server.
13. And the big data risk control server executes big data wind control service, performs data risk control based on the face image data and the safety detection data to obtain a wind control result, and returns the wind control result to the real-name authentication server.
14. And the real-name authentication server performs face detection on the face image data under the condition that a data wind control result returned by the big data risk control server shows that the face image data and the safety detection data have no risk, namely real-name authentication.
Specifically, the face image to be detected can be compared with the face image and the identity information contained in the database with public confidence, if the comparison result shows that the face image to be detected and the identity information are stored in the database, the person is determined to be the person, and real-name authentication is passed; otherwise, the real-name authentication is not passed.
And 15-17, the real-name authentication server returns the real-name authentication result to the user through the camera acquisition and face verification interface of the client.
The present disclosure also provides a real-name authentication device.
Referring to fig. 7, according to a first embodiment of the real-name authentication apparatus provided by the present disclosure, the apparatus includes:
a face image acquisition unit 710 for acquiring face image data;
a configuration information reading unit 720, configured to read configuration information, and obtain a plurality of function modules and a calling sequence that need to be called for the current application scenario configuration; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a flare pupil liveness detection module;
the face detection unit 730 is used for calling a face detection module to perform face detection on the acquired face image data according to the calling sequence;
the face living body detection unit 740 is configured to, after detecting the face information, sequentially call one or more face living body detection modules included in the configuration information according to the call sequence to perform face living body detection;
and the real-name authentication unit 750 is configured to send the face image data to the cloud real-name authentication server for real-name authentication after the face living body detection is passed, so as to obtain an authentication result returned by the cloud real-name authentication server.
By applying the real-name authentication device provided by the disclosure, different multiple function modules and calling sequences can be configured for different application scenes, especially for living human face detection, different living human face detection modules including one or more of a silent living body detection module, an action living body detection module and a dazzling pupil living body detection module can be configured for different application scenes, and therefore the real-name authentication device can adapt to different service application scenes and has high universality. Meanwhile, compared with the mode that different real-name authentication codes need to be developed in different service application scenes in the prior art, the development and maintenance cost is reduced.
Referring to fig. 8, according to a second embodiment of the real-name authentication apparatus provided by the present disclosure, on the basis of the embodiment shown in fig. 7, the apparatus further includes: a device wind control unit 800 and a face quality detection unit 810.
The face quality detection unit 810 may be configured to, after detecting face information, sequentially call one or more face live detection modules included in configuration information according to the call order, and before performing a face live detection step, call the face quality detection modules according to the call order to perform quality detection on the face information; and after the quality detection is passed, executing the step of sequentially calling one or more human face living body detection modules contained in the configuration information to perform human face living body detection.
The device wind control unit 800 is used for risk scanning of the device before the face image data are collected to obtain device risk scanning data; sending the equipment risk scanning data to a cloud big data risk control server to obtain an equipment wind control result returned by the big data risk control server; and under the condition that the equipment wind control result shows that the equipment has no risk, triggering the human face image acquisition unit.
As shown in fig. 8, the real-name authentication unit 750 in the present embodiment may include:
a safety detection data obtaining subunit 751, configured to calculate face image data by using a preset safety algorithm, so as to obtain safety detection data;
a data sending subunit 752, configured to send the encrypted face image data and security detection data to a cloud-based real-name authentication server, so that the cloud-based real-name authentication server sends the face image data and the security detection data to a big-data risk control server, and performs real-name authentication on the face image data when a data wind control result returned by the big-data risk control server indicates that the face image data and the security detection data are risk-free;
and an authentication result obtaining subunit 753, configured to obtain an authentication result returned by the cloud-end real-name authentication server.
In the face living body detection unit in this embodiment, when the current application scene is a logistics traffic scene, the plurality of function modules to be called and the calling sequence include: the method comprises the steps of calling a face detection module, calling a face quality detection module, calling a silence living body detection module and calling an action living body detection module.
Specifically, when the current application scenario is a financial insurance scenario, the multiple function modules and the call sequence that need to be called include: the system comprises a face detection module, a face quality detection module, an action living body detection module and a dazzling pupil living body detection module.
When the current application scene is a social interaction scene, the plurality of function modules to be called and the calling sequence include: the face detection module, the face quality detection module and the silence living body detection module are called to call the action living body detection module.
The present disclosure also provides a real-name authentication system.
Referring to fig. 9, according to a first embodiment of the real name authentication system provided by the present disclosure, the system includes: a client 910 and a cloud real-name authentication server 920;
the client 910 is configured to acquire face image data; reading configuration information, and obtaining a plurality of function modules which are configured aiming at the current application scene and need to be called and a calling sequence; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a flare pupil liveness detection module; according to the calling sequence, calling a face detection module to carry out face detection on the collected face image data; after the face information is detected, one or more face living body detection modules contained in the configuration information are sequentially called according to the calling sequence to carry out face living body detection; after the living human face detection is passed, sending the human face image data to a cloud real-name authentication server;
and the cloud real-name authentication server 920 is configured to perform real-name authentication on the face image data and return an authentication result to the client.
By applying the real-name authentication system provided by the disclosure, different multiple function modules and calling sequences can be configured for different application scenes, especially for living human face detection, different living human face detection modules including one or more of a silent living body detection module, an action living body detection module and a dazzling pupil living body detection module can be configured for different application scenes, and therefore the real-name authentication system can adapt to different service application scenes and has high universality. Meanwhile, compared with the mode that different real-name authentication codes need to be developed in different service application scenes in the prior art, the development and maintenance cost is reduced.
Referring to fig. 10, according to a second embodiment of the real-name authentication system provided by the present disclosure, the system further includes: big data risk control server 1010.
After the living human face detection is passed, the client 910 sends the human face image data to a cloud-based real-name authentication server for real-name authentication, including: calculating the face image data based on a preset safety algorithm to obtain safety detection data; the encrypted face image data and security detection data are sent to the cloud-side real-name authentication server 920.
The real-name authentication server 920 in the cloud performs real-name authentication on the face image data, including: sending the face image data and the security detection data to a big data risk control server 1010; and under the condition that the data wind control result returned by the big data risk control server 1010 shows that the face image data and the safety detection data have no risk, performing real-name authentication on the face image data.
The big data risk control server 1010 may be configured to perform data risk control based on the face image data and the security detection data, obtain a wind control result, and return the wind control result to the real-name authentication server 920.
In other embodiments, the client 910 is further configured to perform risk scanning on the device before acquiring the face image data, so as to obtain device risk scanning data; sending the device risk scan data to a cloud big data risk control server 1010; and under the condition that the equipment wind control result shows that the equipment has no risk, executing the step of acquiring the facial image data.
The big data risk control server 1010 is further configured to perform equipment risk control based on the equipment risk scanning data, obtain an equipment wind control result, and return the equipment wind control result to the client 910.
In this embodiment, before the real-name authentication is performed by the real-name authentication server at the cloud, the device risk control and the data risk control may be performed by the big-data risk control server at the cloud.
Therefore, the human face image data for real-name authentication and the electronic equipment for real-name authentication, such as a mobile phone and the like, have no safety risk, and the safety of the real-name authentication is improved from the two aspects of data and equipment.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 11 shows a schematic block diagram of an example electronic device 1100 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 11, the device 1100 comprises a computing unit 1101, which may perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)1102 or a computer program loaded from a storage unit 1108 into a Random Access Memory (RAM) 1103. In the RAM 1103, various programs and data necessary for the operation of the device 1100 may also be stored. The calculation unit 1101, the ROM 1102, and the RAM 1103 are connected to each other by a bus 1104. An input/output (I/O) interface 1105 is also connected to bus 1104.
A number of components in device 1100 connect to I/O interface 1105, including: an input unit 1106 such as a keyboard, a mouse, and the like; an output unit 1107 such as various types of displays, speakers, and the like; a storage unit 1108 such as a magnetic disk, optical disk, or the like; and a communication unit 1109 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 1109 allows the device 1100 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 1101 can be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 1101 performs the respective methods and processes described above, such as the above-described real-name authentication method. For example, in some embodiments, the real-name authentication method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1100 via ROM 1102 and/or communication unit 1109. When the computer program is loaded into RAM 1103 and executed by the computing unit 1101, one or more steps of the real-name authentication method described above may be performed. Alternatively, in other embodiments, the computing unit 1101 may be configured to perform the above-described real-name authentication method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (20)
1. A real name authentication method, comprising:
collecting face image data;
reading configuration information, and obtaining a plurality of function modules which are configured aiming at the current application scene and need to be called and a calling sequence; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a flare pupil liveness detection module;
according to the calling sequence, calling a face detection module to carry out face detection on the collected face image data;
after the face information is detected, one or more face living body detection modules contained in the configuration information are sequentially called according to the calling sequence to carry out face living body detection;
after the human face living body detection is passed, the human face image data is sent to a cloud real-name authentication server for real-name authentication, and an authentication result returned by the cloud real-name authentication server is obtained.
2. The method of claim 1, wherein,
the plurality of functional modules further include: a face quality detection module;
after the face information is detected, before the step of sequentially calling one or more face living body detection modules included in the configuration information according to the calling sequence to perform face living body detection, the method further includes:
calling a face quality detection module to carry out quality detection on the face information according to the calling sequence; and after the quality detection is passed, executing the step of sequentially calling one or more human face living body detection modules contained in the configuration information to perform human face living body detection.
3. The method of claim 2, wherein,
when the current application scene is a logistics traffic scene, the plurality of function modules to be called and the calling sequence comprise:
the method comprises the steps of calling a face detection module, calling a face quality detection module, calling a silence living body detection module and calling an action living body detection module.
4. The method of claim 2, wherein,
when the current application scene is a financial insurance scene, the plurality of function modules to be called and the calling sequence comprise:
the system comprises a face detection module, a face quality detection module, an action living body detection module and a dazzling pupil living body detection module.
5. The method of claim 2, wherein,
when the current application scene is a social interaction scene, the plurality of function modules to be called and the calling sequence include:
a face detection module, a face quality detection module and a silence living body detection module are called.
6. The method of claim 1, wherein,
among the plurality of function modules configured for the current application scenario that need to be invoked are: in the case of the active living body detection module, the configuration information further includes: two or more kinds of action information which need to be detected by the action living body detection module; and/or the presence of a gas in the gas,
among the plurality of function modules configured for the current application scenario that need to be invoked are: in the case of the dazzle pupil biometric detection module, the configuration information further includes: the detection strategy of the dazzle pupil living body detection module is shown as follows: and presetting strict or normal strategy configuration information.
7. The method of claim 1, wherein,
after the human face living body detection is passed, the step of sending the human face image data to the real-name authentication server at the cloud end for real-name authentication to obtain an authentication result returned by the real-name authentication server at the cloud end comprises the following steps:
calculating the face image data based on a preset safety algorithm to obtain safety detection data;
sending the encrypted face image data and the encrypted safety detection data to a cloud real-name authentication server so that the cloud real-name authentication server sends the face image data and the safety detection data to a big-data risk control server, and performing real-name authentication on the face image data under the condition that a data pneumatic control result returned by the big-data risk control server shows that the face image data and the safety detection data have no risk; and obtaining an authentication result returned by the cloud real-name authentication server.
8. The method of claim 1, further comprising:
before the face image data is collected, risk scanning is carried out on equipment to obtain equipment risk scanning data;
sending the equipment risk scanning data to a cloud big data risk control server to obtain an equipment wind control result returned by the big data risk control server;
and under the condition that the equipment wind control result shows that the equipment has no risk, executing the step of acquiring the facial image data.
9. A real-name authentication apparatus comprising:
the face image acquisition unit is used for acquiring face image data;
the device comprises a configuration information reading unit, a configuration information processing unit and a configuration information processing unit, wherein the configuration information reading unit is used for reading configuration information and obtaining a plurality of function modules which are configured aiming at the current application scene and need to be called and a calling sequence; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a flare pupil liveness detection module;
the face detection unit is used for calling the face detection module to carry out face detection on the collected face image data according to the calling sequence;
the face living body detection unit is used for sequentially calling one or more face living body detection modules contained in the configuration information according to the calling sequence after the face information is detected so as to carry out face living body detection;
and the real-name authentication unit is used for sending the face image data to a real-name authentication server at the cloud end for real-name authentication after the face living body detection is passed, so as to obtain an authentication result returned by the real-name authentication server at the cloud end.
10. The apparatus of claim 9, further comprising: a face quality detection unit;
the plurality of functional modules further include: a face quality detection module;
the face quality detection unit is used for calling one or more face living body detection modules contained in the configuration information in sequence according to the calling sequence after the face information is detected, and calling the face quality detection modules to carry out quality detection on the face information according to the calling sequence before the step of carrying out face living body detection; and after the quality detection is passed, executing the step of sequentially calling one or more human face living body detection modules contained in the configuration information to perform human face living body detection.
11. The apparatus of claim 10, wherein,
when the current application scene is a logistics traffic scene, the plurality of function modules to be called and the calling sequence comprise: calling a face detection module, a face quality detection module, a silence living body detection module and an action living body detection module;
when the current application scene is a financial insurance scene, the plurality of function modules to be called and the calling sequence comprise: calling a face detection module, a face quality detection module, an action living body detection module and a dazzling pupil living body detection module;
when the current application scene is a social interaction scene, the plurality of function modules to be called and the calling sequence include: the face detection module, the face quality detection module and the silence living body detection module are called to call the action living body detection module.
12. The apparatus of claim 9, wherein,
among the plurality of function modules configured for the current application scenario that need to be invoked are: in the case of the active living body detection module, the configuration information further includes: two or more kinds of action information which need to be detected by the action living body detection module; and/or the presence of a gas in the gas,
among the plurality of function modules configured for the current application scenario that need to be invoked are: in the case of the dazzle pupil biometric detection module, the configuration information further includes: the detection strategy of the dazzle pupil living body detection module is shown as follows: and presetting strict or normal strategy configuration information.
13. The apparatus of claim 9, wherein,
the real-name authentication unit comprises:
the safety detection data acquisition subunit is used for calculating the face image data by a preset safety algorithm to acquire safety detection data;
the data sending subunit is used for sending the encrypted face image data and the encrypted safety detection data to a cloud real-name authentication server so that the cloud real-name authentication server sends the face image data and the safety detection data to a big data risk control server, and under the condition that a data wind control result returned by the big data risk control server shows that the face image data and the safety detection data have no risk, the face image data is subjected to real-name authentication;
and the authentication result obtaining subunit is used for obtaining an authentication result returned by the cloud real-name authentication server.
14. The apparatus of claim 9, further comprising,
the equipment wind control unit is used for carrying out risk scanning on the equipment before the face image data are collected to obtain equipment risk scanning data; sending the equipment risk scanning data to a cloud big data risk control server to obtain an equipment wind control result returned by the big data risk control server; and under the condition that the equipment wind control result shows that the equipment has no risk, triggering the human face image acquisition unit.
15. A real name authentication system comprising: a client and a cloud real-name authentication server;
the client is used for acquiring face image data; reading configuration information, and obtaining a plurality of function modules which are configured aiming at the current application scene and need to be called and a calling sequence; wherein, a plurality of functional modules include: the system comprises a face detection module and a face living body detection module; wherein, the human face live body detection module includes: one or more of a silence liveness detection module, an action liveness detection module, and a flare pupil liveness detection module; according to the calling sequence, calling a face detection module to carry out face detection on the collected face image data; after the face information is detected, one or more face living body detection modules contained in the configuration information are sequentially called according to the calling sequence to carry out face living body detection; after the living human face detection is passed, sending the human face image data to a cloud real-name authentication server;
and the cloud real-name authentication server is used for performing real-name authentication on the face image data and returning an authentication result to the client.
16. The system of claim 15, further comprising: a big data risk control server;
after the human face living body detection is passed, the client sends the human face image data to the real-name authentication server at the cloud end for real-name authentication, and the real-name authentication comprises the following steps: calculating the face image data based on a preset safety algorithm to obtain safety detection data; sending the encrypted face image data and the encrypted safety detection data to a cloud real-name authentication server;
the real-name authentication server at the cloud end performs real-name authentication on the face image data, and comprises the following steps: sending the face image data and the safety detection data to a big data risk control server; performing real-name authentication on the face image data under the condition that a data wind control result returned by the big data risk control server shows that the face image data and the safety detection data have no risk;
and the big data risk control server is used for carrying out data risk control based on the face image data and the safety detection data to obtain a wind control result, and returning the wind control result to the cloud real-name authentication server.
17. The system of claim 16, wherein,
the client is also used for risk scanning of the equipment before the face image data are collected to obtain equipment risk scanning data; sending the equipment risk scanning data to a big data risk control server at the cloud end; under the condition that the equipment wind control result shows that the equipment has no risk, executing the step of acquiring the face image data;
and the big data risk control server is also used for carrying out equipment risk control based on the equipment risk scanning data to obtain an equipment wind control result and returning the equipment wind control result to the client.
18. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
19. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
20. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.
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