Artificial intelligence service system and method for realizing service

Abstract

The invention is suitable for the field of intelligent services, and provides an artificial intelligence service system and a service realization method, and the system comprises a user identity binding module which is used for binding a user identity with number taking information through a face recognition and multi-mode verification technology, and generating a dynamic user tag; the real-time monitoring module is used for analyzing the position and state of the user in real time based on a distributed camera and an AI behavior detection model, and updating a user tag; the dynamic queue management module is used for intelligently adjusting the queue sequence according to the state change of the user tag and triggering the number calling logic; and the notification display module is used for synchronously updating the display screen information through the visual interface and displaying the queue adjustment result. The service efficiency is improved, the waiting time is reduced, the user experience is enhanced, and the method is particularly suitable for orderly management and service optimization in a high-flow scene in a peak period.

Description

Technical Field

[0001] This invention relates to the field of intelligent services, specifically to an artificial intelligence service system and a method for implementing such services. Background Technology

[0002] In order to provide services to people in a more orderly manner, banks or other organizations will be equipped with queuing and calling systems, so that each window or counter can be used more rationally.

[0003] Existing queuing systems generally employ a static queue management mechanism, where users retain their numbers in a fixed order even if they leave the lobby after taking one. This rigid queue logic leads to resource waste (such as idle windows) and low service efficiency (such as subsequent users having to wait for invalid numbers), which can easily cause user anxiety and complaints, especially during peak business hours.

[0004] Furthermore, static queues cannot dynamically release invalid numbers, forcing window service staff to wait even when users haven't arrived, further exacerbating the imbalance between resource idleness and user waiting time. For example, in banking transactions, when a user takes a number and leaves, at least one window remains idle for a short period. This is not an isolated incident and significantly impacts overall service efficiency and customer experience. Therefore, this paper proposes an artificial intelligence service system and its implementation method to address these issues. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an artificial intelligence service system and a method for implementing the service, so as to solve the problems existing in the above-mentioned background technology.

[0006] This invention is implemented as follows: an artificial intelligence service system, the system comprising:

[0007] The user identity binding module is used to bind user identity with number retrieval information through facial recognition and multimodal verification technology, and generate dynamic user tags;

[0008] The real-time monitoring module is used to analyze the user's location and status in real time based on distributed cameras and AI behavior detection models, and update the user's tags.

[0009] The dynamic queue management module is used to intelligently adjust the queue order based on changes in user tag status and trigger the calling logic.

[0010] The notification display module is used to display the queue adjustment results by synchronously updating the display screen information through a visual interface.

[0011] As a further aspect of the present invention: the user identity binding module includes:

[0012] The information collection unit is used to acquire the user's identity information, image, and number retrieval information. The identity information is obtained by scanning the ID card through a self-service terminal.

[0013] The multimodal authentication unit is used to identify features of a person's image using AI recognition technology and combine it with the scanned identity information to authenticate the user, thereby ensuring that the number information matches the actual user.

[0014] The dynamic tag production unit is used to create a user tag containing the current status for the user based on the authentication result and the number retrieval information, and associate the user tag with the number retrieval information;

[0015] The data management unit is used to securely store the verified user number information and corresponding dynamic tags in the database after encryption.

[0016] As a further aspect of the present invention: the multimodal authentication unit includes:

[0017] The feature recognition subunit is used to recognize the user's face based on the image of the person;

[0018] The direct verification subunit is used to extract facial features from a person's image and compare them with the photos stored in the identity information when the image contains a face.

[0019] The information supplementation subunit is used to separate the user's full-body outline from the image of a person when the image does not contain a face, and to extract the user's shape features.

[0020] The data association subunit is used to generate shape feature codes based on the extracted shape features and associate the shape feature codes with identity information.

[0021] As a further aspect of the present invention: the real-time monitoring module includes:

[0022] The video acquisition unit is used to acquire video streams in real time through cameras deployed at various locations and to perform preliminary processing;

[0023] The tracking and recording unit is used to detect and continuously track the positional changes of each user in the video stream using AI algorithms, in order to form a continuous record of behavioral trajectories;

[0024] The behavior recognition unit is used to analyze user actions based on a deep learning model to determine the user's current state;

[0025] The tag update unit is used to dynamically update the user tags based on the behavior recognition results.

[0026] As a further aspect of the present invention: the dynamic queue management module includes:

[0027] The queue adjustment unit is used to intelligently adjust the queue structure based on the received changes in user tags.

[0028] Individual tagging unit, used to tag the queue number corresponding to the user when the user leaves;

[0029] The queue hiding and synchronization unit is used to automatically hide the marked queue number and synchronize it to the calling system when a user's queue number is called and the user's tag has not changed.

[0030] The data clearing unit is used to continuously monitor the user tag based on a set time value. When the user tag does not change, the information associated with the user tag will be deleted.

[0031] Another object of the present invention is to provide a method for implementing artificial intelligence services, the method comprising the following steps:

[0032] By using facial recognition and multimodal verification technologies, user identity is bound to queuing information, and dynamic user tags are generated.

[0033] Based on distributed cameras and AI behavior detection models, the system analyzes user location and status in real time and updates user tags.

[0034] The queue order is intelligently adjusted based on changes in the user's tag status, and the calling logic is triggered accordingly.

[0035] The queue adjustment results are displayed by synchronously updating the screen information through a visual interface.

[0036] As a further aspect of the present invention: the step of binding user identity with queuing information through face recognition and multimodal verification technology, and generating dynamic user tags, specifically includes:

[0037] The system obtains the user's identity information, image, and ticket number information, wherein the identity information is obtained by scanning the ID card at a self-service terminal.

[0038] AI recognition technology is used to identify features in human images, and combined with scanned identity information to verify the user's identity, in order to ensure that the number information matches the actual user;

[0039] Based on the identity verification result and the number retrieval information, a user tag containing the current status is created for the user, and the user tag is associated with the number retrieval information;

[0040] Verified user ID numbers and their corresponding dynamic tags are encrypted and securely stored in the database.

[0041] As a further aspect of the present invention: the step of using AI recognition technology to perform feature recognition on a person's image and combining it with scanned identity information to verify the user's identity specifically includes:

[0042] Facial recognition of users based on images of people;

[0043] When a person's image contains a face, the facial features in the image are extracted and compared with the photos stored in the identity information.

[0044] When the image of a person does not contain a face, the object detection algorithm is used to separate the user's full-body outline from the image of the person and extract the user's shape features;

[0045] Based on the extracted shape features, a shape feature code is generated, and the shape feature code is associated with the identity information.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] This invention achieves efficient and intelligent service process optimization by integrating four modules: user identity binding, real-time monitoring, dynamic queue management, and notification display. When users obtain a queue number, multimodal verification technology ensures accurate matching between the number and the actual user, generating dynamic user tags that reflect real-time changes in user status. Based on distributed cameras and AI behavior detection models, the system can analyze user location and status in real time and automatically update user tags, thus ensuring the accuracy and timeliness of queue information. The dynamic queue management module intelligently adjusts the queue order based on these tag changes, ensuring effective utilization of service resources. Finally, the notification display module synchronously updates the queue information on the screen through a visual interface, allowing all users to clearly understand the latest queue status and their own position, improving information transparency and overall service experience satisfaction. This integrated solution not only improves service efficiency and reduces waiting time but also enhances user experience, making it particularly suitable for orderly management and service optimization in high-traffic scenarios during peak periods. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the structure of an artificial intelligence service system.

[0049] Figure 2 This is a schematic diagram of the user identity binding module in an artificial intelligence service system.

[0050] Figure 3 This is a schematic diagram of the structure of a multimodal authentication unit in an artificial intelligence service system.

[0051] Figure 4 This is a schematic diagram of the structure of a real-time monitoring module in an artificial intelligence service system.

[0052] Figure 5 This is a schematic diagram of the dynamic queue management module in an artificial intelligence service system.

[0053] Figure 6 A flowchart of a method for implementing an artificial intelligence.

[0054] Figure 7 A flowchart for generating dynamic user tags as a method for implementing artificial intelligence.

[0055] Figure 8 A flowchart illustrating user authentication as a method for implementing artificial intelligence. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0057] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0058] like Figure 1 As shown, this embodiment of the invention provides an artificial intelligence service system, the system comprising:

[0059] The user identity binding module 100 is used to bind user identity with number retrieval information through face recognition and multimodal verification technology, and generate dynamic user tags;

[0060] The real-time monitoring module 200 is used to analyze the user's location and status in real time based on distributed cameras and AI behavior detection models, and update the user's tags.

[0061] The dynamic queue management module 300 is used to intelligently adjust the queue order based on changes in the user's tag status and trigger the call logic.

[0062] The notification display module 400 is used to display the queue adjustment results by synchronously updating the display screen information through a visual interface.

[0063] It should be noted that existing queuing systems generally use a static queue management mechanism. Even if a user leaves the lobby after taking a number, the system retains that number in a fixed order. This rigid queue logic leads to wasted resources, and when necessary, staff have to manually skip invalid numbers. Therefore, the existing service system has long had some problems.

[0064] In this embodiment of the invention, the integration of four modules—user identity binding, real-time monitoring, dynamic queue management, and notification display—achieves efficient and intelligent service process optimization. When a user obtains a number, multimodal verification technology ensures accurate matching between the number and the actual user, generating dynamic user tags that reflect real-time changes in user status. Based on distributed cameras and an AI behavior detection model, the system can analyze the user's location and status in real time and automatically update user tags, thus ensuring the accuracy and timeliness of queuing information. The dynamic queue management module intelligently adjusts the queue order based on these tag changes, ensuring effective utilization of service resources. Finally, the notification display module synchronously updates the queuing information on the screen through a visual interface, allowing all users to clearly understand the latest queuing status and their own position, improving information transparency and overall service experience satisfaction. This integrated solution not only improves service efficiency and reduces waiting time but also enhances user experience, making it particularly suitable for orderly management and service optimization in high-traffic scenarios during peak periods.

[0065] like Figure 2 As shown, in a preferred embodiment of the present invention, the user identity binding module 100 includes:

[0066] The information collection unit 101 is used to acquire the user's identity information, image of the person, and number retrieval information. The identity information is obtained by scanning the certificate through the self-service terminal.

[0067] The multimodal authentication unit 102 is used to perform feature recognition on the image of a person using AI recognition technology, and to verify the identity of the user by combining the scanned identity information, so as to ensure that the number information matches the actual user.

[0068] The dynamic tag production unit 103 is used to create a user tag containing the current status for the user based on the authentication result and the number retrieval information, and associate the user tag with the number retrieval information;

[0069] The data management unit 104 is used to securely store the verified user number information and the corresponding dynamic tag in the database after encryption.

[0070] In this embodiment of the invention, users scan their ID cards or other identification documents at a self-service terminal. The system obtains their identity information in this way, while simultaneously capturing an image of the user and recording their queue number. Next, the system uses artificial intelligence technology to recognize facial features in the image and compares the recognition results with the scanned identity information to verify the authenticity of the user's identity and prevent impersonation. After successful verification, the system generates a user tag with status information (such as "waiting" or "verified") based on the current verification result and queue number information, and associates this tag with the corresponding queue number for subsequent tracking and scheduling. Finally, all verified user information and corresponding tags are encrypted and stored in a database, ensuring data security and providing a reliable data source for other functional modules of the system (such as the real-time monitoring module and the dynamic queue management module). For example, in a bank lobby, if a user leaves after taking a number, the system can adjust the queue order based on the tag status, and automatically restore the original position upon returning, achieving more efficient and flexible service management.

[0071] like Figure 3 As shown, in a preferred embodiment of the present invention, the multimodal authentication unit 102 includes:

[0072] The feature recognition subunit 112 is used to recognize the user's face based on the image of the person;

[0073] The direct verification subunit 122 is used to extract facial features from a person image and compare them with the photos stored in the identity information when the person image contains a face.

[0074] The information supplementation subunit 132 is used to separate the user's full-body outline from the image of a person when the image does not contain a face, and to extract the user's shape features.

[0075] The data association subunit 142 is used to generate a shape feature code based on the extracted shape features and associate the shape feature code with the identity information.

[0076] In this embodiment of the invention, after a user obtains a number and enters the identity verification process, the system first identifies the user's face based on the captured image. If facial information is clearly visible in the image, facial features are extracted and compared with photos stored on ID cards or other identification documents to determine if they belong to the same person. In certain special cases (such as when the user is looking down, has their face turned to the side, or is obscured), if facial information cannot be obtained from the image, the system activates a target detection algorithm to separate the user's full-body outline from the image and extract shape features, including clothing color, attire, and backpack. Subsequently, the system converts these shape features into "shape feature codes" that can be used for identification and associates them with the previously scanned identity information as supplementary identity verification criteria. This combination of facial recognition and shape feature recognition not only improves the accuracy of identity verification but also enhances the system's adaptability in complex scenarios, such as for subsequent seamless tracking and tag updates during queuing, thereby improving the overall security and intelligence of the service.

[0077] like Figure 4 As shown, in a preferred embodiment of the present invention, the real-time monitoring module 200 includes:

[0078] The video acquisition unit 201 is used to acquire video streams in real time by deploying cameras at various locations and perform preliminary processing.

[0079] The tracking and recording unit 202 is used to detect and continuously track the position changes of each user in the video stream using AI algorithms, in order to form a continuous record of behavioral trajectories;

[0080] The behavior recognition unit 203 is used to analyze the user's actions based on a deep learning model to determine the user's current state;

[0081] The tag update unit 204 is used to dynamically update the user tags based on the behavior recognition results.

[0082] In this embodiment of the invention, the system collects user video streams through cameras deployed at various key locations and performs preliminary processing such as noise reduction and cropping to improve the accuracy of subsequent analysis. Then, it uses AI algorithms to detect each user in the frame and continuously tracks their position changes to form a complete movement trajectory. Next, it analyzes the user's actions based on a deep learning model to determine their current state, such as "waiting," "getting up to leave," or "heading to a window." Finally, based on the identified behavioral state, the system automatically updates the user's tag information, such as changing the state from "waiting" to "left," thus providing a basis for subsequent queue adjustments and service scheduling. For example, in a government service hall, when the system detects that a user has left the waiting area, it will promptly update their tag to avoid queuing delays caused by invalid numbers, thereby improving overall service efficiency and intelligence.

[0083] like Figure 5 As shown, in a preferred embodiment of the present invention, the dynamic queue management module 300 includes:

[0084] The queue adjustment unit 301 is used to intelligently adjust the queue structure based on the received changes in user tags.

[0085] Individual marking unit 302 is used to mark the queue number corresponding to the user when the user leaves;

[0086] The queue hiding and synchronization unit 303 is used to automatically hide the marked queue number and synchronize it to the calling system when the user's queue number is called and the user's tag has not changed.

[0087] The data clearing unit 304 is used to continuously monitor the user tag based on a set time value, and delete the information associated with the user tag when the user tag does not change.

[0088] In this embodiment of the invention, after receiving the status update information of the user tag (such as the user leaving or returning), the queue adjustment unit intelligently adjusts the current queue structure to ensure that the queue always reflects the order of the actual users present. When a user is detected to have temporarily left, the individual tagging unit marks the original queue number, retains the user's identity information, but no longer uses the user as the current call target. If the user has not returned when the number is called, the queue hiding and synchronization unit automatically hides the marked number from the display screen and synchronizes it to the calling system, skipping the number to avoid wasting time waiting. At the same time, the data clearing unit continuously monitors the user tag. If no status change is detected within a set time (i.e., the user has not returned), the associated number, tag, and other information is automatically cleared to release system resources and maintain the timeliness and accuracy of the data.

[0089] like Figure 6 As shown in the figure, this embodiment of the invention also provides a method for implementing artificial intelligence services, the method comprising the following steps:

[0090] The S100 uses facial recognition and multimodal verification technology to bind user identity with number retrieval information and generate dynamic user tags.

[0091] The S200 uses distributed cameras and an AI behavior detection model to analyze user location and status in real time and update user tags.

[0092] The S300 intelligently adjusts the queue order based on changes in the user's tag status and triggers the call-number logic.

[0093] The S400 displays the queue adjustment results by synchronously updating the display screen information through a visual interface.

[0094] In this embodiment of the invention, firstly, when a user obtains a queue number, facial recognition and multimodal authentication technology are used to bind the user's actual identity with the obtained queue number, generating a dynamic user tag containing the current status (such as "waiting" or "left"), providing accurate identity information for subsequent service scheduling. Subsequently, the system utilizes cameras deployed in different areas and AI behavior detection models to capture and analyze user location changes and behavior status in real time, continuously updating user tags to ensure that queue information always reflects the true situation. When a user's status changes (such as leaving or returning), the system intelligently adjusts the queue order based on the latest tag information, automatically skipping invalid numbers or restoring the user's original position, and triggering the corresponding calling logic to ensure the efficient operation of the service window. Finally, all queue adjustment results are synchronously displayed on the screen through a visual interface, allowing users to clearly understand the current queuing progress and improving information transparency and service satisfaction.

[0095] like Figure 7 As shown in the preferred embodiment of the present invention, the step of binding user identity with number retrieval information through face recognition and multimodal verification technology and generating dynamic user tags specifically includes:

[0096] S101, Obtain the user's identity information, image, and number retrieval information, wherein the identity information is obtained by scanning the document through the self-service terminal;

[0097] S102 uses AI recognition technology to identify features in a person's image and combines it with scanned identity information to verify the user's identity, thereby ensuring that the number information matches the actual user.

[0098] S103, Based on the authentication result and the number retrieval information, create a user tag containing the current status for the user, and associate the user tag with the number retrieval information;

[0099] S104. The verified user number information and the corresponding dynamic tag are encrypted and securely stored in the database.

[0100] like Figure 8 As shown, in a preferred embodiment of the present invention, the step of using AI recognition technology to perform feature recognition on a person's image and combining it with scanned identity information to verify the user's identity specifically includes:

[0101] S112, Recognize the user's face based on the image of the person;

[0102] S122, When the image of a person contains a face, extract the facial features from the image of the person and compare them with the photo stored in the identity information.

[0103] S132, When the image of a person does not contain a face, the object detection algorithm is used to separate the user's full-body outline from the image of the person and extract the user's shape features;

[0104] S142, Generate a shape feature code based on the extracted shape features, and associate the shape feature code with the identity information.

[0105] The above description only details the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0106] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0107] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0108] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. An artificial intelligence service system, characterized in that, The system includes: The user identity binding module is used to bind user identity with number retrieval information through facial recognition and multimodal verification technology, and generate dynamic user tags; The real-time monitoring module is used to analyze the user's location and status in real time based on distributed cameras and AI behavior detection models, and update the user's tags. The dynamic queue management module is used to intelligently adjust the queue order based on changes in user tag status and trigger the calling logic. The notification display module is used to display the queue adjustment results by synchronously updating the display screen information through a visual interface.

2. The artificial intelligence service system according to claim 1, characterized in that, The user identity binding module includes: The information collection unit is used to acquire the user's identity information, image, and number retrieval information. The identity information is obtained by scanning the ID card through a self-service terminal. The multimodal identity verification unit is used to identify features of a person's image using AI recognition technology and combine it with the scanned identity information to verify the user's identity, thereby ensuring that the number information matches the actual user. The dynamic tag production unit is used to create a user tag containing the current status for the user based on the authentication result and the number retrieval information, and associate the user tag with the number retrieval information; The data management unit is used to securely store the verified user number information and corresponding dynamic tags in the database after encryption.

3. The artificial intelligence service system according to claim 2, characterized in that, The multimodal authentication unit includes: The feature recognition subunit is used to recognize the user's face based on the image of the person; The direct verification subunit is used to extract facial features from a person's image and compare them with the photos stored in the identity information when the image contains a face. The information supplementation subunit is used to separate the user's full-body outline from the image of a person when the image does not contain a face, and to extract the user's shape features. The data association subunit is used to generate shape feature codes based on the extracted shape features and associate the shape feature codes with identity information.

4. The artificial intelligence service system according to claim 1, characterized in that, The real-time monitoring module includes: The video acquisition unit is used to acquire video streams in real time through cameras deployed at various locations and to perform preliminary processing; The tracking and recording unit is used to detect and continuously track the positional changes of each user in the video stream using AI algorithms, in order to form a continuous record of behavioral trajectories; The behavior recognition unit is used to analyze user actions based on a deep learning model to determine the user's current state; The tag update unit is used to dynamically update the user tags based on the behavior recognition results.

5. The artificial intelligence service system according to claim 1, characterized in that, The dynamic queue management module includes: The queue adjustment unit is used to intelligently adjust the queue structure based on the received changes in user tags. Individual tagging unit, used to tag the queue number corresponding to the user when the user leaves; The queue hiding and synchronization unit is used to automatically hide the marked queue number and synchronize it to the calling system when a user's queue number is called and the user's tag has not changed. The data clearing unit is used to continuously monitor the user tag based on a set time value. When the user tag does not change, the information associated with the user tag will be deleted.

6. A method for implementing artificial intelligence services, characterized in that, The method includes the following steps: By using facial recognition and multimodal verification technologies, user identity is bound to queuing information, and dynamic user tags are generated. Based on distributed cameras and AI behavior detection models, the system analyzes user location and status in real time and updates user tags. The queue order is intelligently adjusted based on changes in the user's tag status, and the calling logic is triggered accordingly. The queue adjustment results are displayed by synchronously updating the screen information through a visual interface.

7. The method for implementing artificial intelligence services according to claim 6, characterized in that, The steps of binding user identity with queuing information and generating dynamic user tags through facial recognition and multimodal verification technology specifically include: The system obtains the user's identity information, image, and ticket number information, wherein the identity information is obtained by scanning the ID card at a self-service terminal. AI recognition technology is used to identify features in human images, and combined with scanned identity information to verify the user's identity, in order to ensure that the number information matches the actual user; Based on the authentication result and the number retrieval information, a user tag containing the current status is created for the user, and the user tag is associated with the number retrieval information; Verified user ID numbers and their corresponding dynamic tags are encrypted and securely stored in the database.

8. The method for implementing artificial intelligence services according to claim 7, characterized in that, The steps of using AI recognition technology to identify features in a person's image and combining this with scanned identity information to verify the user's identity specifically include: Facial recognition of users based on images of people; When a person's image contains a face, the facial features in the image are extracted and compared with the photos stored in the identity information. When the image of a person does not contain a face, the object detection algorithm is used to separate the user's full-body outline from the image of the person and extract the user's shape features; Based on the extracted shape features, a shape feature code is generated, and the shape feature code is associated with the identity information.

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