Application in taxi-hailing software risk control system based on face recognition strategy

Abstract

The invention relates to application of a face recognition strategy in a taxi-hailing software risk control system, and relates to the technical field of risk control strategies, basic information of a driver is obtained through an identity recognition interface of a user side, a current driving scene is recognized, a database is inquired according to the identity of the driver and the current scene, and the risk control strategy of the current scene is matched; judging whether the driver needs to carry out face verification or not, when the driver needs to carry out face verification, requesting a token from a face recognition service, enabling the driver to carry out face recognition according to a prompt, enabling a camera to capture a face image and upload the face image to a server side, receiving a face recognition result by the server side, judging whether the identity verification of the driver is passed or not, and informing the driver of a verification result; on the basis of traditional driver identity authentication and monitoring, face recognition verification is added, a risk control strategy is combined, and whether face verification needs to be carried out or not can be dynamically judged in different scenes, so that real-time monitoring and risk management are realized, and the intelligent level of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of risk control strategies. More specifically, the present invention relates to an application of a face recognition strategy in a risk control system of a taxi-hailing software. Background Art

[0002] In modern taxi services, with the continuous increase in users and service providers, the safety and behavior supervision of drivers have become particularly important. The taxi-hailing platform not only needs to provide safe and convenient travel services for passengers, but also needs to ensure the authenticity and reliability of drivers' identities to prevent potential safety hazards and fraud. Traditional identity verification methods, such as SMS verification codes and password input, although can ensure safety to a certain extent, have obvious deficiencies in terms of efficiency and security. SMS verification codes are vulnerable to interception or forgery, while password input may lead to security risks due to users forgetting or leaking them.

[0003] The introduction of face recognition technology provides a new idea for solving these problems. As an advanced biometric technology, face recognition can confirm an individual's identity by analyzing and comparing facial features. This technology not only improves the accuracy of identity verification, but also significantly enhances the verification efficiency. The driver only needs to perform a simple face recognition through the camera, and the system can complete the identity confirmation within a few seconds, avoiding the cumbersome input process and improving the user experience.

[0004] An application of a face recognition strategy in a risk control system of a taxi-hailing software realizes the identity authentication and monitoring of driver users by dynamically configuring face verification channels and scenario strategies, and at the same time supports various rule combination methods according to the accessed service provider platform, specified city, etc. Summary of the Invention

[0005] The present invention aims at the technical problems existing in the prior art, and provides an application of a face recognition strategy in a risk control system of a taxi-hailing software to solve the problems raised in the above background art.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: An application of a face recognition strategy in a risk control system of a taxi-hailing software specifically includes the following steps:

[0007] Step 101: Obtain the basic information of the driver through the identity recognition interface of the user terminal and identify the current driving scenario;

[0008] Step 102: Query the database according to the driver's identity and the current scenario, match the risk control strategy of the current scenario, and determine whether the driver needs to perform face verification;

[0009] Step 103: When the driver needs to perform face verification, request a token from the face recognition service. The driver performs face recognition according to the prompts, and the camera captures the facial image and uploads it to the server.

[0010] Step 104: The server receives the face recognition result, determines whether the driver's identity verification is passed, and notifies the driver of the verification result.

[0011] In a preferred embodiment, in step 101, the basic information of the driver is obtained through the identity recognition interface of the user terminal, and the current driving scenario is recognized. The specific steps are as follows:

[0012] Step A1: User login: The driver opens the taxi-hailing application, enters the login credentials for identity verification, and the application calls the identity recognition interface to send a login request.

[0013] Step A2: Information acquisition: After the identity recognition interface verifies the validity of the credentials, it returns the basic information of the driver, including: name, mobile phone number, ID number, registration time, affiliated platform and city. The obtained basic information of the driver is stored in the local state of the application for subsequent use.

[0014] Step A3: Driving scenario recognition: Obtain the current geographical location coordinates of the driver through GPS positioning to determine whether the driver is in the driving area, and check the current status of the driver in the application to confirm whether the driver has logged in and is in a state where orders can be received. When the driver is in the effective driving area and there is no pending order request, it is recognized as the "driving scenario"; when the driver is in the effective area and there is a pending order request, it is recognized as the "order receiving scenario".

[0015] In a preferred embodiment, in step 102, according to the driver's identity and the current scenario, query the database and match the risk control strategy for the current scenario to determine whether the driver needs to perform face verification. The specific steps are as follows:

[0016] Step B1: Query the risk control strategy: According to the driver's identity information and the current scenario, construct the conditions for the query request, and send the constructed query request to the risk control strategy database to obtain the query result. The query request includes whether the driver's identity has been verified, whether the current scenario is in the order receiving state, and whether the driver has any violation records. The query result includes the risk control strategy ID, strategy description, and face verification requirements. Among them, the risk control strategy ID is the unique identifier of the risk control strategy that matches the current query conditions; the strategy description is a detailed description of the risk control strategy. In the "driving scenario", face verification is required when the driver has violation records. In the "order receiving scenario", face verification is not required when the driver's identity is normal; the face verification requirements include whether face verification is required.

[0017] In a preferred embodiment, the face verification requirement is judged by the rule parameters of the face sampling inspection strategy, and further includes the following steps:

[0018] Step S1, confirm the sampling inspection times and time intervals: Check whether the current time meets the sampling inspection conditions, and specifically check the following parameters:

[0019] (a) The minimum interval time T1 from the last non-sampling inspection, where T1 is at least 1 hour;

[0020] (b) The minimum time interval T2 from the last sampling inspection, where T2 is at least 1 hour;

[0021] (c) Whether the number of sampling inspections has reached the upper limit Nm, where Nm is at most 3 times;

[0022] Step S2, random sampling inspection judgment: According to the set random sampling hit probability A% = 50%, decide whether to conduct a sampling inspection. Randomly generate a number λ. When λ ≤ A%, conduct face verification;

[0023] Step S3, dynamically identify the sampling inspection ratio: Confirm that the ratio B% of applying dynamic recognition sampling inspection in the current scenario is 0%, that is, no dynamic recognition sampling inspection is carried out;

[0024] Step B2, according to the queried risk control strategy, judge whether face verification is required. When the current scenario is the "vehicle departure scenario" and the driver has a violation record, face verification is required; when the current scenario is the "order receiving scenario" and the driver's identity is normal, face verification is not required.

[0025] In a preferred embodiment, in step 103, when the driver needs to conduct face verification, request a token from the face recognition service. The driver conducts face recognition according to the prompt. The camera captures the facial image and uploads it to the server. The token is a temporary credential for identity verification and authorization. The specific steps are as follows:

[0026] Step C1, the driver sends a request to the face recognition server to obtain a token, including identity information and request parameters, and sends a prompt to the driver to inform that face recognition is required. The driver starts the camera to conduct face recognition according to the prompt and captures the driver's facial image. The prompt content includes the purpose, operation steps and precautions of face recognition;

[0027] Step C2, upload the captured facial image to the server of the face recognition service. The upload request contains the previously obtained token. The face recognition server processes the uploaded image, including image preprocessing, feature extraction and comparison with the facial features stored in the database, and returns the recognition result, including whether the verification is successful and the recognized identity information.

[0028] In a preferred embodiment, in step 104, the server receives the face recognition result, determines whether the driver's identity verification is passed, and informs the driver of the verification result. The specific steps are as follows:

[0029] Step D1, receiving the recognition result: The server receives the recognition result from the face recognition service, including: verification status, recognized identity information, and error information that cannot be recognized. The server parses the received recognition result and extracts key information, including whether the verification is successful and the driver's identity information;

[0030] Step D2, judging the identity verification result: According to the parsed verification status, the server makes a judgment: when the verification status is "successful", it confirms that the driver's identity is valid; when the verification status is "failed", it confirms that the driver's identity is invalid;

[0031] Step D3, generating feedback information: According to the result of the identity verification, generate feedback information, including: verification result and relevant prompt information, such as continuing to operate after success and needing to re-verify after failure, and inform the driver of the generated feedback information by means of message push.

[0032] The beneficial effects of the present invention are as follows: Obtain the driver's basic information through the identity recognition interface of the user terminal, identify the current driving scenario, query the database and match the risk control strategy of the current scenario according to the driver's identity and the current scenario, judge whether the driver needs to perform face verification. When the driver needs to perform face verification, request a token from the face recognition service. The driver performs face recognition according to the prompt, the camera captures the facial image and uploads it to the server. The server receives the face recognition result, determines whether the driver's identity verification is passed, and informs the driver of the verification result. Based on the traditional driver identity authentication and monitoring, the present invention adds face recognition verification, combines with the risk control strategy, can dynamically judge whether face verification is needed in different scenarios, thus realizing real-time monitoring and risk management, improving the intelligent level of the system. Based on the design and implementation of the face recognition scenario strategy, the security of the risk control system of the taxi software is improved, and the real-time and security of the driver identity verification and monitoring are increased. Face recognition verification is safer, more efficient than SMS verification codes and password methods, and is also more convenient for drivers to operate. Brief Description of the Drawings

[0033] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiment

[0034] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0035] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more, unless otherwise specifically defined.

[0036] In the description of the present application, the term "for example" is used to mean "used as an example, illustration, or explanation". Any embodiment described as "for example" in the present application is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present application.

[0037] Embodiment 1

[0038] This embodiment provides an application of a face recognition strategy in a risk control system of a taxi-hailing software as shown in Figure 1 and specifically includes the following steps:

[0039] Step 101: Obtain the basic information of the driver through the identity recognition interface of the user terminal and identify the current driving scenario;

[0040] Step 102: Query the database according to the identity of the driver and the current scenario, match the risk control strategy for the current scenario, and determine whether the driver needs to perform a face verification;

[0041] Step 103: When the driver needs to perform a face verification, request a token from the face recognition service. The driver performs a face recognition according to the prompt, and the camera captures the facial image and uploads it to the server;

[0042] Step 104: The server receives the face recognition result, determines whether the identity verification of the driver is passed, and informs the driver of the verification result.

[0043] Preferably, in step 101, the basic information of the driver is obtained through the identity recognition interface of the user terminal, and the current driving scenario is recognized, which can effectively confirm the real identity of the driver and reduce the risk of identity forgery. The specific steps are as follows:

[0044] Step A1, User login: The driver opens the taxi-hailing application and enters the login credentials for identity verification. The application calls the identity recognition interface and sends a login request, which reduces the cumbersome manual input and waiting time and improves the overall user experience;

[0045] Step A2, Information acquisition: After the identity recognition interface verifies the validity of the credentials, it returns the basic information of the driver, including: name, mobile phone number, ID number, registration time, affiliated platform and city. The obtained basic information of the driver is stored in the local state of the application for subsequent use;

[0046] Step A3, Driving scenario recognition: The current geographical location coordinates of the driver are obtained through GPS positioning to determine whether the driver is in the driving area, and the current status of the driver in the application is checked to confirm whether the driver has logged in and is in the order-receiving state. When the driver is in the effective driving area and there is no pending order request, it is recognized as the "driving scenario"; when the driver is in the effective area and there is a pending order request, it is recognized as the "order-receiving scenario".

[0047] Preferably, in step 102, according to the driver's identity and the current scenario, the risk control strategy for the current scenario is queried in the database to determine whether the driver needs to undergo face verification, so as to achieve more accurate risk management. The specific steps are as follows:

[0048] Step B1, Query risk control strategy: According to the driver's identity information and the current scenario, the conditions for constructing the query request are built, and the constructed query request is sent to the risk control strategy database to obtain the query result. The query request includes whether the driver's identity has been verified, whether the current scenario is the order-receiving state, and whether the driver has any violation records. The query result includes the risk control strategy ID, strategy description, and face verification requirements. Among them, the risk control strategy ID is the unique identifier of the risk control strategy that matches the current query conditions; the strategy description is a detailed description of the risk control strategy. In the "driving scenario", face verification is required when the driver has violation records. In the "order-receiving scenario", face verification is not required when the driver's identity is normal, which avoids unnecessary face verification, improves the driver's usage experience, and reduces interference; the face verification requirements include whether face verification is required; through the clear risk control strategy, the driver and passengers can understand the necessity of verification, enhance the trust in the platform, and improve user loyalty. Among them, the face verification requirements are judged through the rule parameters of the face sampling strategy, and further include the following steps:

[0049] Step S1, confirm the number of sampling inspections and the time interval: Check whether the current time meets the sampling inspection conditions, specifically check the following parameters:

[0050] (a) the minimum interval time T1 from the last non-spot inspection, where T1 is at least 1 hour;

[0051] (b) the minimum time interval T2 from the last random inspection, where T2 is at least 1 hour;

[0052] (c) Whether the number of random inspections has reached the upper limit Nm, where Nm is a maximum of 3 times;

[0053] Step S2, random sampling judgment: according to the set random sampling hit probability A%=50%, decide whether to conduct random sampling, randomly generate a number λ, and when λ≤A%, perform face verification;

[0054] Step S3, dynamic identification sampling ratio: confirm that the ratio of dynamic identification sampling applied in the current scenario is B%=0%, that is, no dynamic identification sampling is performed;

[0055] Step B2: Based on the queried risk control strategy, determine whether facial verification is required. If the current scenario is the "vehicle dispatch scenario" and the driver has a violation record, facial verification is required; if the current scenario is the "order acceptance scenario" and the driver's identity is normal, facial verification is not required.

[0056] Preferably, in step 103, when the driver needs to perform face verification, he requests a token from the face recognition service, and the driver performs face recognition according to the prompts. The camera captures the facial image and uploads it to the server, which can effectively prevent identity fraud and ensure that only the real driver can complete the identity verification. The token is a temporary credential used for identity verification and authorization. The specific steps are as follows:

[0057] Step C1, the driver sends a request to the face recognition server to obtain a token, including identity information and request parameters, and sends a prompt to the driver to inform him that face recognition is required. The driver follows the prompt and starts the camera to perform face recognition to capture the driver's facial image. The prompt content includes the purpose, operation steps and precautions of face recognition. When performing face recognition, the driver can clearly understand the necessity and process of verification, enhance the trust in the platform, and face recognition reduces the reliance on manual review, improves efficiency, and reduces the risk of human error;

[0058] Step C2: Upload the captured facial image to the server of the face recognition service. The upload request includes the previously obtained token. The face recognition service server processes the uploaded image, including image preprocessing, feature extraction, and comparison with the facial features stored in the database, and returns the recognition result, including whether the verification is successful and the recognized identity information. Through a secure token mechanism, it ensures that the facial image is protected during the upload process and reduces the risk of data leakage.

[0059] Preferably, in step 104, the server receives the face recognition result, determines whether the driver's identity verification is passed, and informs the driver of the verification result. By accurately judging the driver's identity, it ensures that only verified drivers can receive orders, reduces security risks, and protects the safety of passengers and drivers. The specific steps are as follows:

[0060] Step D1: Receive the recognition result: The server receives the recognition result from the face recognition service, and the content includes: verification status, recognized identity information, and error information that cannot be recognized. The server analyzes the received recognition result and extracts key information, including whether the verification is successful and the driver's identity information;

[0061] Step D2: Judge the identity verification result: According to the parsed verification status, the server makes a judgment: when the verification status is "success", it confirms that the driver's identity is valid; when the verification status is "failure", it confirms that the driver's identity is invalid;

[0062] Step D3: Generate feedback information: According to the result of the identity verification, generate feedback information, and the content includes: verification result and relevant prompt information, including continuing to operate after success and needing to re-verify after failure, and inform the driver of the generated feedback information through message push, which improves the security and efficiency of identity verification.

[0063] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0064] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0066] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufacture including instruction means, and the instruction means implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0068] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0069] Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An application of a face recognition strategy in a risk control system of a taxi-hailing software, characterized in that, Specifically, it includes the following steps: Step 101: Obtain the driver's basic information through the identity recognition interface of the user terminal and identify the current driving scenario; Step 102: According to the driver's identity and the current scenario, query the database and match the risk control strategy for the current scenario to determine whether the driver needs to undergo face verification; Step 103: When the driver needs to undergo face verification, request a token from the face recognition service. The driver performs face recognition according to the prompts, and the camera captures the facial image and uploads it to the server; Step 104: The server receives the face recognition result, determines whether the driver's identity verification is passed, and informs the driver of the verification result.

2. The application of a face recognition strategy in a risk control system of a taxi-hailing software according to claim 1, characterized in that: In step 101, to obtain the driver's basic information through the identity recognition interface of the user terminal and identify the current driving scenario, the specific steps are as follows: Step A1: User login: The driver opens the taxi-hailing application, enters the login credentials for identity verification, and the application calls the identity recognition interface to send a login request; Step A2: Information acquisition: After verifying the validity of the credentials, the identity recognition interface returns the driver's basic information, including: name, mobile phone number, ID number, registration time, affiliated platform and city, and stores the obtained driver's basic information in the local state of the application; Step A3: Driving scenario recognition: Obtain the driver's current geographical location coordinates through GPS positioning to determine whether he is in the driving area, and check the driver's current status in the application to confirm whether he has logged in and is in the order-receiving state. When the driver is in the effective driving area and there is no pending order request, it is recognized as the "driving scenario"; when the driver is in the effective area and there is a pending order request, it is recognized as the "order-receiving scenario".

3. The application of a face recognition strategy in a risk control system of a taxi-hailing software according to claim 1, wherein, The steps of querying the database and matching the risk control strategy for the current scenario according to the driver's identity and the current scenario to determine whether the driver needs to undergo face verification are as follows: Step B1: Query the risk control strategy: According to the driver's identity information and the current scenario, construct the conditions for the query request, and send the constructed query request to the risk control strategy database to obtain the query result. The query request includes whether the driver's identity has been verified, whether the current scenario is in the order-receiving state, and whether the driver has any violation records. The query result includes the risk control strategy ID, strategy description, and face verification requirements. Among them, the risk control strategy ID is the unique identifier of the risk control strategy that matches the current query conditions; the strategy description is a detailed description of the risk control strategy. In the "driving scenario", face verification is required when the driver has violation records. In the "order-receiving scenario", face verification is not required when the driver's identity is normal; the face verification requirements include whether face verification is required; Step B2: According to the queried risk control strategy, determine whether face verification is required. When the current scenario is the "driving scenario" and the driver has violation records, face verification is required; when the current scenario is the "order-receiving scenario" and the driver's identity is normal, face verification is not required.

4. The application of a face recognition strategy in a risk control system of a taxi-hailing software according to claim 3, wherein The face verification requirements are judged by the rule parameters of the face sampling strategy, and further include the following steps: Step S1. Confirm the sampling inspection times and time intervals: Check whether the current time meets the sampling inspection conditions, and specifically check the following parameters: (a) The minimum interval time T1 from the last non-sampling inspection, where T1 ≥ 1 hour; (b) The minimum time interval T2 from the last sampling inspection, where T2 ≥ 1 hour; (c) Whether the number of sampling inspections has reached the upper limit Nm, where Nm ≤ 3 times; Step S2. Random sampling inspection judgment: According to the set random sampling hit probability A% = 50%, decide whether to conduct a sampling inspection. Randomly generate a number λ. When λ ≤ A%, conduct face verification; Step S3. Dynamically identify the sampling inspection ratio: Confirm that the ratio B% of applying dynamic recognition sampling inspection in the current scenario is 0%, that is, no dynamic recognition sampling inspection is conducted.

5. The application of a face recognition strategy in a risk control system of a taxi-hailing software according to claim 1, characterized in that, In the said step 103, when the driver needs to conduct face verification, request a token from the face recognition service. The driver conducts face recognition according to the prompt. The camera captures the facial image and uploads it to the server. The specific steps are as follows: Step C1. The driver sends a request to the face recognition server to obtain a token, including identity information and request parameters, and sends a prompt to the driver, informing that face recognition is required. The driver starts the camera to conduct face recognition according to the prompt and captures the facial image of the driver; Step C2. Upload the captured facial image to the server of the face recognition service. The upload request contains the previously obtained token. The face recognition service server processes the uploaded image, including image preprocessing, feature extraction, and comparison with the facial features stored in the database, and returns the recognition result, including whether the verification is successful and the recognized identity information.

6. The application of a face recognition strategy in a risk control system of a ride-hailing software according to claim 1, characterized in that, In the said step 104, the server receives the face recognition result, judges whether the driver's identity verification is passed, and informs the driver of the verification result. The specific steps are as follows: Step D1. Receive the recognition result: The server receives the recognition result from the face recognition service, and the content includes: verification status, recognized identity information, and error information that cannot be recognized. The server analyzes the received recognition result and extracts the key information, including whether the verification is successful and the driver's identity information; Step D2. Judge the identity verification result: According to the analyzed verification status, the server judges: When the verification status is "successful", confirm that the driver's identity is valid; when the verification status is "failed", then confirm that the driver's identity is invalid; Step D3. Generate feedback information: According to the result of the identity verification, generate feedback information, and the content includes: verification result and relevant prompt information. The said relevant prompt information includes continuing to operate after success and needing to re-verify after failure, and push the generated feedback information to the driver by means of message push.

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