Face recognition method and device, electronic equipment and computer readable medium

Abstract

Embodiments of the present disclosure disclose a face recognition method and device, an electronic device and a computer readable medium. A specific embodiment of the method comprises: in response to receiving face recognition request information, determining a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information as a target face recognition interface type; determining preset interface configuration information corresponding to the target face recognition interface type as target interface configuration information; performing detection processing on each face recognition interface to obtain a detection result information set; generating target face recognition interface information according to the detection result information set and a face recognition interface information sequence; generating face recognition result information according to the target face recognition interface information and a target user face image; and sending the face recognition result information to a face recognition terminal. The embodiment can reduce the number of repeated interface connections and shorten the time spent.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of computer, and particularly, to a face recognition method and device, an electronic device and a computer readable medium. BACKGROUND

[0002] In the process of building a smart campus, face recognition technology has great significance for solving a series of campus problems such as campus safety and attendance. At present, when face recognition is performed, the commonly used way is: for face recognition terminals in different scenarios, a face recognition interface corresponding to the face recognition terminal is called to perform face recognition, and a face recognition result is directly returned to the face recognition terminal.

[0003] However, the inventors have found that when face recognition is performed in the above manner, the following technical problems often exist:

[0004] First, the specifications and standards of different face recognition interfaces are not the same, and when the number of face recognition requests is high and concurrent, the upper limit value of the face recognition interface second query rate cannot be uniformly set, resulting in a large number of repeated interface connections and a long time consumption.

[0005] Second, the number of face recognition requests is not the same at different time periods, and the upper limit value of the face recognition interface second query rate is the same for different time periods. When the number of face recognition requests corresponding to the current time period is small, the face recognition interface with a high priority is always processing requests, and the face recognition interface with a low priority is idle, resulting in waste of load resources.

[0006] Third, since the obtained face recognition result involves sensitive information such as user identity information and images, directly returning the obtained face recognition result to the face recognition terminal leads to leakage of user sensitive information and low security of user information.

[0007] The above information disclosed in this BACKGROUND section is only for the purpose of enhancing the understanding of the background of the present inventive concepts, and therefore, it can contain information that is not prior art known to those of ordinary skill in the art in the country to which this patent belongs. SUMMARY

[0008] The summary of the present disclosure is used to introduce the concepts in a brief manner, which will be described in detail in the following detailed description. The summary of the present disclosure is not intended to identify key or essential features of the claimed technology nor is it intended to be used to limit the scope of the claimed technology.

[0009] Some embodiments of the present disclosure propose a face recognition method, device, electronic device and computer readable medium to solve one or more of the technical problems mentioned in the above BACKGROUND section.

[0010] In a first aspect, some embodiments of the present disclosure provide a face recognition method, which comprises: in response to receiving face recognition request information, determining, as a target face recognition interface type, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information, wherein the face recognition request information comprises a target user face image; determining, as target interface configuration information, preset interface configuration information corresponding to the target face recognition interface type, wherein the target interface configuration information comprises a face recognition interface information sequence, and the face recognition interface information sequence corresponds to each face recognition interface; performing detection processing on the face recognition interfaces to obtain a detection result information set; generating target face recognition interface information according to the detection result information set and the face recognition interface information sequence; generating face recognition result information according to the target face recognition interface information and the target user face image; and sending the face recognition result information to a face recognition terminal corresponding to the face recognition request information.

[0011] In a second aspect, some embodiments of the present disclosure provide a face recognition device, which comprises: a first determination unit configured to determine, as a target face recognition interface type, a preset face recognition interface type corresponding to a face recognition type included in face recognition request information in response to receiving the face recognition request information, wherein the face recognition request information comprises a target user face image; a second determination unit configured to determine, as target interface configuration information, preset interface configuration information corresponding to the target face recognition interface type, wherein the target interface configuration information comprises a face recognition interface information sequence, and the face recognition interface information sequence corresponds to each face recognition interface; a detection unit configured to perform detection processing on the face recognition interfaces to obtain a detection result information set; a first generation unit configured to generate target face recognition interface information according to the detection result information set and the face recognition interface information sequence; a second generation unit configured to generate face recognition result information according to the target face recognition interface information and the target user face image; and a sending unit configured to send the face recognition result information to a face recognition terminal corresponding to the face recognition request information.

[0012] In a third aspect, some embodiments of the present disclosure provide an electronic device, which comprises: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the implementations of the first aspect.

[0013] In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0014] The above various embodiments of the present disclosure have the following beneficial effects: the face recognition method of some embodiments of the present disclosure can reduce the number of repeated interface connections and shorten the time consumed. Specifically, the reason for the large number of repeated interface connections and the long time consumed is that the specifications and standards of different face recognition interfaces are also different, and when the number of face recognition requests is high and concurrent, it is impossible to uniformly set the upper limit value of the face recognition interface query rate, resulting in a large number of repeated interface connections and a long time consumed. Based on this, the face recognition method of some embodiments of the present disclosure first determines, in response to receiving face recognition request information, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information as a target face recognition interface type. The face recognition request information includes a target user face image. Thus, the type of the interface for face recognition of the target user face image can be obtained. Secondly, the preset interface configuration information corresponding to the target face recognition interface type is determined as target interface configuration information. The target interface configuration information includes a face recognition interface information sequence. The face recognition interface information sequence corresponds to each face recognition interface. Thus, different face recognition interfaces for the same face recognition type can be uniformly set, thereby reducing the number of repeated interface connections. Then, each face recognition interface is detected to obtain a detection result information group. Thus, it can be determined whether each face recognition interface can be normally invoked. Subsequently, according to the detection result information group and the face recognition interface information sequence, target face recognition interface information is generated. Thus, the information of the face recognition interface for face recognition of the target user face image can be obtained, thereby enabling face recognition. Then, according to the target face recognition interface information and the target user face image, face recognition result information is generated. Thus, the face recognition operation can be completed. Finally, the face recognition result information is sent to a face recognition terminal corresponding to the face recognition request information. Thus, the face recognition terminal can obtain the face recognition result, and perform related operations according to the face recognition result information. Also, because the different face recognition interfaces corresponding to the same face recognition type are uniformly set when performing face recognition, the number of repeated interface connections is reduced, thereby reducing the number of repeated interface connections and shortening the time consumed. BRIEF DESCRIPTION OF DRAWINGS

[0015] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail some embodiments thereof with reference to the annexed drawings. In the drawings:

[0016] Figure 1 is a flowchart of some embodiments of a face recognition method according to the present disclosure;

[0017] Figure 2 is a structural schematic diagram of some embodiments of a face recognition apparatus according to the present disclosure;

[0018] Figure 3 is a structural schematic diagram of an electronic device suitable for use to implement some embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in detail with reference to the drawings, wherein the same or similar components are denoted by the same or similar reference numerals, and thus repeated description is omitted. As such, the embodiments of the present disclosure can be implemented in various forms, and should not be construed as being limited to the embodiments set forth herein. Rather, the embodiments are provided so that the present disclosure will be more thoroughly understood and complete. It should be understood that the drawings and embodiments of the present disclosure are only for illustrative purposes and should not be construed as limiting the scope of the present disclosure.

[0020] It should also be noted that only parts related to the present application are shown in the drawings for the purpose of description. Embodiments in the present disclosure and features in the embodiments can be combined with each other without conflict.

[0021] It should be noted that the terms "first", "second", and the like in the present disclosure are only used to distinguish different devices, modules, or units, and do not limit the order or interdependence of the functions performed by these devices, modules, or units.

[0022] It should be noted that the terms "one", "multiple" in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless otherwise explicitly stated in the context, it should be understood as "one or more".

[0023] The names of messages or information exchanged between the devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0024] The collection, storage, use, etc. of user personal information (such as user face images, user identity information) involved in the present disclosure, before performing the corresponding operation, the relevant organization or individual fulfills the obligations including carrying out personal information security impact assessment, fulfilling the notice obligation to the personal information subject, obtaining the authorization consent of the personal information subject in advance, etc., and complying with the provisions of relevant laws and regulations.

[0025] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0026] Figure 1 Flow 100 of some embodiments of the face recognition method according to the present disclosure is shown. The face recognition method includes the following steps:

[0027] Step 101, in response to receiving face recognition request information, determining the preset face recognition interface type corresponding to the face recognition type included in the face recognition request information in the preset face recognition interface type set as the target face recognition interface type.

[0028] In some embodiments, an execution subject (e.g., a computing device) of a face recognition method can determine, in response to receiving face recognition request information, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information as a target face recognition interface type. The face recognition request information can be information requesting face recognition. The face recognition request information can be sent by a face recognition terminal. The face recognition terminal can be a terminal for collecting a face image. For example, the face recognition terminal can be a mobile phone or a camera device. The camera device can include, but is not limited to, a camera and a controller. The face recognition request information can include, but is not limited to, a target user face image, preset request information, and a face recognition type. The target user face image can be an image of a face of a target user. The target user can be a user who needs to perform face recognition. The preset request information can be information representing a request for face recognition. The face recognition type can represent a face recognition manner. The face recognition type can be, but is not limited to, one of the following: one-to-one face recognition and one-to-many face recognition. The one-to-one face recognition can be determining a similarity between the target user face image and a preset target user face image to achieve face recognition. The preset target user face image can be a target user face image uploaded by the target user. The one-to-many face recognition can be matching the target user face image with each preset face image in a preset face image library to obtain a preset face image with the highest similarity to the target user face image as a recognition result. The preset face recognition interface type in the preset face recognition interface type set can be a pre-set type of interface for face recognition. The preset face recognition interface type set can include, but is not limited to, at least one of the following: one-to-one face recognition interface and one-to-many face recognition interface. The one-to-one face recognition interface can be an interface for implementing one-to-one face recognition. The one-to-many face recognition interface can be an interface for implementing one-to-many face recognition. In practice, the execution subject can determine, in response to receiving the face recognition request information, the preset face recognition interface type corresponding to the face recognition type included in the face recognition request information as the target face recognition interface type in various ways.

[0029] Optionally, the face recognition request information can further include an application platform identifier. The application platform identifier can be a unique identifier of an application platform. The application platform can be an application platform that needs to be logged in to collect a face image by a face recognition terminal.

[0030] In some optional implementations of some embodiments, the execution subject can determine the preset face recognition interface type corresponding to the face recognition type included in the face recognition request information as the target face recognition interface type by the following steps:

[0031] Firstly, the platform permission type corresponding to the application platform identifier included in the face recognition request information is determined. In practice, the execution subject can determine the preset application platform identifier identical to the application platform identifier included in the face recognition request information in the preset application platform identifier set as the target application platform identifier. The preset application platform identifier set can be a set of identifiers of various application platforms corresponding to various face recognition terminals. The face recognition terminals in the various face recognition terminals can correspond to the application platforms in the various application platforms one by one. The preset application platform identifier included in the preset application platform identifier set can be a preset application platform identifier. Then, the preset permission type corresponding to the target application platform identifier is determined as the platform permission type. The preset permission type can be a preset permission type. The permission type can represent whether the application platform can perform face recognition through the face recognition interface. The permission type can be one of the following, but is not limited to: authorized, unauthorized. The authorized can represent that the application platform can perform face recognition through the face recognition interface. The unauthorized can represent that the application platform cannot perform face recognition through the face recognition interface.

[0032] Secondly, in response to determining that the permission type meets the preset permission condition, the preset face recognition interface type corresponding to the face recognition type included in the face recognition request information in the preset face recognition interface type set is determined as the target face recognition interface type. The preset permission condition can be that the permission type represents that the application platform can perform face recognition through the face recognition interface.

[0033] In some optional implementations of some embodiments, the execution subject can determine the preset face recognition interface type corresponding to the face recognition type included in the face recognition request information as the target face recognition interface type by the following steps in response to receiving the face recognition request information:

[0034] Firstly, the face detection processing is performed on the target user face image to obtain a face region image. In practice, the execution subject can perform face detection processing on the target user face image by a preset face detection algorithm to obtain a face region image. The preset face detection algorithm can be a preset face detection algorithm for detecting faces. For example, the preset face detection algorithm can be a face detection algorithm based on a convolutional neural network.

[0035] Secondly, a face size of the face region image is determined. In practice, the execution subject can determine the size of the face region image as the face size.

[0036] Thirdly, a face sharpness of the face region image is determined. In practice, the execution subject can perform a sharpness evaluation process on the face region image by using a preset image sharpness evaluation algorithm to obtain the face sharpness. The preset image sharpness evaluation algorithm can be, but is not limited to, one of the following: Tenengrad gradient method, Laplacian gradient method, and variance method.

[0037] Fourthly, a facial feature detection process is performed on the face region image to obtain a facial feature region image set. In practice, the execution subject can perform a facial feature detection process on the face region image by using a preset facial feature image detection algorithm to obtain a facial feature detection image set. The preset facial feature image detection algorithm can be a facial feature image detection algorithm that is preset for detecting facial features of a face. For example, the preset facial feature image detection algorithm can be a convolutional neural network-based image detection algorithm.

[0038] Fifthly, a face completeness degree is determined according to the facial feature region image set. In practice, for each facial feature region image in the facial feature region image set, the execution subject can determine the facial feature region image as a complete facial feature region image in response to determining whether the size of the facial feature region image is within a preset size range of a corresponding preset facial feature type. The preset facial feature type can be a type that is preset for representing a facial feature. The preset facial feature type can be, but is not limited to, one of the following: ear, eyebrow, eye, nose, and mouth. The preset size range can be a range of sizes that is preset for the corresponding preset facial feature type.

[0039] For example, when the preset facial feature type corresponding to the facial feature region image is a nose, the size of the facial feature region image is (50*65), and the preset size range of the nose is (50*70, 60*80), the facial feature region image is determined as a complete facial feature region image.

[0040] Then, the number of each complete facial feature region image that is determined is determined as a complete facial feature number. Finally, a ratio of the complete facial feature number to the number of each facial feature region image included in the facial feature region image set is determined as the face completeness degree.

[0041] In the sixth step, the face quality score information is generated according to the face size, the face definition, and the face integrity. In practice, first, the execution subject can normalize the length value and the width value of the face size respectively to obtain a normalized length value and a normalized width value. Second, a product of the normalized length value and a preset length weight coefficient is determined as a first normalized value. The preset length weight coefficient can be a weight coefficient corresponding to a preset length value. Then, a product of the normalized width value and a preset width weight coefficient is determined as a second normalized value. The preset width weight coefficient can be a weight coefficient corresponding to a preset width value. The sum of the preset length weight coefficient and the preset width weight coefficient can be 1. After that, the sum of the first normalized value and the second normalized value is determined as a face size score. Next, the face definition is determined as a definition score. Then, the face integrity is determined as an integrity score. Finally, the sum of a product of the face size score and a preset size weight coefficient, a product of the definition score and a preset definition weight coefficient, and a product of the integrity score and a preset integrity weight coefficient is determined as the face quality score information. The preset size weight coefficient can be a weight coefficient corresponding to a preset face size dimension. For example, the preset size weight coefficient can be 0.2. The preset definition weight coefficient can be a weight coefficient corresponding to a preset face definition dimension. For example, the preset definition weight coefficient can be 0.4. The preset integrity weight coefficient can be a weight coefficient corresponding to a preset face integrity dimension. For example, the preset integrity weight coefficient can be 0.4.

[0042] In the seventh step, in response to determining that the face quality score information satisfies a preset score condition, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information is determined as the target face recognition interface type in the preset face recognition interface type set. The preset score condition can be that the face quality score information is greater than or equal to a preset quality score. The preset quality score can be a preset face quality score representing a qualified face image. The face quality score can be a score of the quality of the face image.

[0043] In step 102, the preset interface configuration information corresponding to the target face recognition interface type is determined as the target interface configuration information.

[0044] In some embodiments, the execution subject can determine preset interface configuration information corresponding to the target face recognition interface type as the target interface configuration information. The preset interface configuration information can be preset configuration information of an interface corresponding to the face recognition interface type. The preset interface configuration information can include but is not limited to an interface identifier, a face recognition interface information sequence, and a preset upper limit of a per-second query rate. The interface identifier can be a unique identifier of the interface corresponding to the face recognition interface type. The face recognition interface information sequence can be a sequence of face recognition interface information sorted in descending order of the priority of the face recognition interface according to the preset priority. The face recognition interface information sequence corresponds to each face recognition interface. The face recognition interface information in the face recognition interface information sequence can correspond to the face recognition interface one by one. The face recognition interface information in the face recognition interface information sequence can be information corresponding to the face recognition interface. The face recognition interface information can include but is not limited to a face recognition interface identifier. The face recognition interface identifier can be a unique identifier of the face recognition interface. The preset upper limit of the per-second query rate can be a preset upper limit of the per-second query rate corresponding to the interface. For example, the preset upper limit of the per-second query rate can be 1000 QPS (Queries Per Second). Each face recognition interface included in the face recognition interface can be an interface of a face recognition model corresponding to the face recognition interface type. The face recognition model can be a model for face recognition pre-trained by a producer (for example, a manufacturer) of the face recognition terminal. The target interface configuration information can include but is not limited to the face recognition interface information sequence.

[0045] In step 103, each face recognition interface is detected to obtain a detection result information set.

[0046] In some embodiments, the execution subject can detect each face recognition interface to obtain a detection result information set. In practice, first, for each face recognition interface included in the face recognition interface, the execution subject can perform the following sub-steps:

[0047] In the first sub-step, the face recognition interface is called.

[0048] In the second sub-step, in response to determining that the face recognition interface calling is successful, preset normal operation information is determined as the detection result information. The preset normal operation information can represent that the face recognition interface is normal. For example, the preset normal operation information can be “normal”.

[0049] A third sub-step of determining preset abnormal operation information as the detection result information in response to determining that the face recognition interface call fails. The preset abnormal operation information can represent face recognition interface abnormality. For example, the preset abnormal operation information can be "abnormal".

[0050] Then, the determined detection result information is combined into a detection result information group. The combination manner can be character concatenation.

[0051] Step 104 of generating target face recognition interface information according to the detection result information group and the face recognition interface information sequence.

[0052] In some embodiments, the execution subject can generate target face recognition interface information according to the detection result information group and the face recognition interface information sequence. The target face recognition interface information can be face recognition interface information corresponding to a target face recognition interface. The target face recognition interface can be a face recognition interface used for face recognition of the target user face image. In practice, the execution subject can generate target face recognition interface information according to the detection result information group and the face recognition interface information sequence in various manners.

[0053] In some optional implementations of some embodiments, the execution subject can generate target face recognition interface information according to the detection result information group and the face recognition interface information sequence by the following steps:

[0054] First, for each detection result information in the detection result information group, the face recognition interface information corresponding to the detection result information is determined as abnormal face recognition interface information in response to determining that the detection result information satisfies a preset abnormal operation condition. The preset abnormal operation condition can be that the detection result information represents face recognition interface abnormality.

[0055] Second, each face recognition interface information corresponding to each determined abnormal face recognition interface information in the face recognition interface information sequence is deleted to obtain a normal operation face recognition interface information sequence.

[0056] Third, target face recognition interface information is generated according to the normal operation face recognition interface information sequence. In practice, the execution subject can generate target face recognition interface information according to the normal operation face recognition interface information sequence in various manners.

[0057] In some optional implementations of some embodiments, the execution subject can generate target face recognition interface information according to the normal operation face recognition interface information sequence by the following steps:

[0058] In practice, the execution subject can determine 1 as the initial value.

[0059] In the second step, for the running normal face recognition interface information sequence and the initial value, the following determination steps are performed.

[0060] In the first determination step, the running normal face recognition interface information arranged at the initial value in the running normal face recognition interface information sequence is determined as the first running normal face recognition interface information.

[0061] In the second determination step, the face recognition interface corresponding to the first running normal face recognition interface information is determined as the first face recognition interface.

[0062] In the third determination step, the second query rate threshold information corresponding to the first face recognition interface is determined. In practice, the execution subject can determine the preset second query rate threshold as the second query rate threshold information corresponding to the first face recognition interface. The preset second query rate threshold can be a preset upper limit value of the second query rate corresponding to the first face recognition interface.

[0063] In some optional implementations of some embodiments, the execution subject can determine the second query rate threshold information corresponding to the first face recognition interface through the following sub-steps:

[0064] In the first sub-step, the time period type corresponding to the current time is determined. In practice, the execution subject can determine the preset time period type corresponding to the current time from the preset time period type set corresponding to each preset time period as the time period type. Each preset time period in the preset time period set can correspond to a preset time period type. The preset time period in the preset time period set can be a preset time period. It should be noted that the interval duration corresponding to the preset time period in the preset time period set is the same. For example, the preset time period can be from 11:00 to 13:00. The preset time period type can represent whether the number of face recognition request information in the corresponding time period is greater than a preset number. The preset number can be a preset number. The preset time period type can be one of, but not limited to, the following: peak period, off-peak period. The peak period represents that the number of face recognition request information in the corresponding time period is greater than or equal to the preset number. The off-peak period represents that the number of face recognition request information in the corresponding time period is less than the preset number. The matching with the current time can be the time period in which the current time is located.

[0065] A second sub-step is to determine the interface resource information corresponding to the first face recognition interface and the time period type as current interface resource information. The interface resource information can be information about the running resources of the interface. The interface resource information can include, but is not limited to, the interface CPU (Central Processing Unit) usage rate and the interface memory usage rate. The interface CPU usage rate can be the CPU usage rate of the interface on the corresponding server. The interface memory usage rate can be the memory usage rate of the interface on the corresponding server.

[0066] A third sub-step is to perform feature extraction processing on the current interface resource information to obtain a current interface resource feature vector. In practice, the execution subject can perform feature extraction processing on the current interface resource information by using a preset feature extraction algorithm to obtain a current interface resource feature vector. The preset feature extraction algorithm can be a pre-set feature extraction algorithm. For example, the feature extraction algorithm can be a principal component analysis method.

[0067] A fourth sub-step is to determine the cosine similarity between the current interface resource feature vector and each preset interface resource feature vector in the preset interface resource feature vector set corresponding to the first face recognition interface as target similarity information. The preset interface resource feature vector in the preset interface resource feature vector set is a pre-set interface resource feature vector.

[0068] A fifth sub-step is to determine the target similarity information that meets a preset similarity condition from the determined target similarity information as matching similarity information. The preset similarity condition can be that the target similarity information is the maximum value among the target similarity information.

[0069] A sixth sub-step is to determine the preset interface resource feature vector corresponding to the matching similarity information as a target preset interface resource feature vector.

[0070] A seventh sub-step is to determine the preset second query rate proportion information corresponding to the target preset interface resource feature vector as target proportion information. The preset second query rate proportion information can be the ratio of the pre-set upper limit value of the current second query rate to the pre-set second query rate threshold.

[0071] An eighth sub-step is to determine the product of the pre-set second query rate threshold and the target proportion information as second query rate threshold information.

[0072] The technical solutions and related contents thereof serve as one of the inventive points of the embodiments of the present disclosure, and solve the second technical problem mentioned in the background art, i.e., the number of face recognition requests in different time periods is not the same, the upper limit value of the second query rate of the face recognition interface is the same for different time periods, when the number of face recognition requests corresponding to the current time period is small, the face recognition interface with a high priority always processes requests, the face recognition interface with a low priority is idle, and load resources are wasted. The factors that lead to the waste of load resources are often as follows: the number of face recognition requests in different time periods is not the same, the upper limit value of the second query rate of the face recognition interface is the same for different time periods, when the number of face recognition requests corresponding to the current time period is small, the face recognition interface with a high priority always processes requests, the face recognition interface with a low priority is idle, and load resources are wasted. If the above factors are solved, the effect of reducing the waste of load resources can be achieved. In order to achieve this effect, the face recognition method of some embodiments of the present disclosure first determines the time period type corresponding to the current time. In this way, it can be determined whether the number of face recognition requests in the time period in which the current time is located is concentrated, so as to determine the upper limit value of the second query rate of the first face recognition interface. Secondly, the interface resource information corresponding to the first face recognition interface and the time period type are determined as the current interface resource information. In this way, the running condition of the first face recognition interface can be obtained. Then, the feature extraction processing is performed on the current interface resource information to obtain a current interface resource feature vector. In this way, the feature vector of the current interface resource information can be obtained. After that, for each preset interface resource feature vector in the preset interface resource feature vector set corresponding to the first face recognition interface, the similarity between the current interface resource feature vector and the preset interface resource feature vector is determined as target similarity information. In this way, each target similarity information can be obtained. Next, the target similarity information that meets the preset similarity condition in the determined target similarity information is determined as matching similarity information. In this way, the target similarity information with the largest similarity can be selected. Then, the preset interface resource feature vector corresponding to the matching similarity information is determined as a target preset interface resource feature vector. In this way, the preset interface resource feature vector with the largest similarity to the current interface resource feature vector can be obtained. After that, the preset second query rate proportion information corresponding to the target preset interface resource feature vector is determined as target proportion information. In this way, the proportion of the upper limit value of the second query rate corresponding to the time period type to the upper limit value of the preset second query rate can be obtained, so that when the number of face recognition requests corresponding to the current time period is small, the upper limit value of the second query rate of the interface can be reduced. Finally, the product of the preset second query rate threshold value and the target proportion information is determined as second query rate threshold value information.Therefore, the upper limit value of the second query rate of the current time can be less than or equal to the upper limit value of the second query rate set in advance, so that when the number of face recognition requests corresponding to the current time period is small, the number of requests of the face recognition interface with a higher priority can be reduced, and the number of requests of the face recognition interface with a lower priority can be increased. Because the upper limit value of the second query rate of the face recognition interface changes according to the type of the time period, when the number of face recognition requests corresponding to the current time period is small, the number of requests of the face recognition interface with a higher priority can be reduced, and the number of requests of the face recognition interface with a lower priority can be increased, so that the number of idle face recognition interfaces can be reduced, thereby reducing the waste of the load resources of the face recognition interface with a lower priority.

[0073] The fourth determining step is to determine the number of to-be-processed requests corresponding to the first face recognition interface.

[0074] The fifth determining step is to determine whether the first running normal face recognition interface information meets a preset interface condition according to the second query rate threshold information and the number of to-be-processed requests. The preset interface condition can be that the number of to-be-processed requests is less than or equal to the second query rate threshold information.

[0075] The sixth determining step is to determine the first running normal face recognition interface information as the target face recognition interface information in response to determining that the first running normal face recognition interface information meets the preset interface condition.

[0076] Optionally, the execution subject can further perform the following determining step:

[0077] The seventh determining step is to continue to perform the above determining step in response to determining that the first running normal face recognition interface information does not meet the preset interface condition. In practice, the execution subject can continue to perform the above determining step by adding 1 to the initial value in response to determining that the first running normal face recognition interface information does not meet the preset interface condition.

[0078] Step 105 is to generate face recognition result information according to the target face recognition interface information and the target user face image.

[0079] In some embodiments, the execution subject can generate face recognition result information according to the target face recognition interface information and the target user face image. In practice, the execution subject can take the target user face image as an input parameter of the face recognition interface corresponding to the target face recognition interface information, call the face recognition interface corresponding to the target face recognition interface information, and obtain the face recognition result information.

[0080] In some optional implementations of some embodiments, the execution subject can generate the face recognition result information according to the target face recognition interface information and the target user face image by the following steps:

[0081] Firstly, the face recognition interface corresponding to the target face recognition interface information is invoked according to the target user face image to obtain a face recognition result. In practice, the execution subject takes the target user face image as an input parameter of the face recognition interface corresponding to the target face recognition interface information, invokes the face recognition interface corresponding to the target face recognition interface information, and obtains a face recognition result. The face recognition result can represent whether the target user face image is matched successfully.

[0082] Secondly, the target user identity information corresponding to the face recognition result, the target user face image, and the face recognition result are determined as the face recognition result information in response to determining that the face recognition result meets a preset recognition result condition. The preset recognition result condition can be that the face recognition result can represent that the target user face image is matched successfully. The target user identity information can represent the identity of the target user. The target user identity information can include but is not limited to the user name and the user ID number.

[0083] Step 106, the face recognition result information is sent to the face recognition terminal corresponding to the face recognition request information.

[0084] In some embodiments, the execution subject can send the face recognition result information to the face recognition terminal corresponding to the face recognition request information. In practice, the execution subject can send the face recognition result information to the face recognition terminal corresponding to the face recognition request information in various ways.

[0085] In some optional implementations of some embodiments, the execution subject can send the face recognition result information to the face recognition terminal corresponding to the face recognition request information by the following steps:

[0086] Firstly, the face detection processing is performed on the target user face image included in the face recognition result information to obtain a face region image. In practice, the execution subject can perform the face detection processing on the target user face image included in the face recognition result information by using the preset face detection algorithm to obtain a face region image.

[0087] Secondly, the execution body can perform blur processing on the face region image according to a preset image blur processing algorithm to obtain a blurred face image. The preset image blur processing algorithm can be a Gaussian blur algorithm.

[0088] Thirdly, the execution body can perform character detection processing on the target user identity information included in the face recognition result information to obtain a to-be-encrypted character group. Specifically, the execution body can determine each intermediate character in a user name included in the target user identity information as a first to-be-encrypted character group. The intermediate character can be a character that is neither the first nor the last character in the user name. Then, the execution body can determine each character in the last six digits of an ID number included in the target user identity information as a second to-be-encrypted character group. Finally, the execution body can combine the first to-be-encrypted character group and the second to-be-encrypted character group to obtain the to-be-encrypted character group.

[0089] Fourthly, the execution body can perform encryption processing on each to-be-encrypted character in the to-be-encrypted character group included in the target user identity information to obtain encrypted identity information. The encryption processing can be performed by using a preset encryption algorithm. The preset encryption algorithm can be an SM2 algorithm.

[0090] Fifthly, the execution body can update the face recognition result information according to the blurred face image and the encrypted identity information to obtain updated face recognition result information as the face recognition result information. Specifically, the execution body can update the target user face image included in the face recognition result information to the blurred face image, and update the target user identity information included in the face recognition result information to the encrypted identity information to obtain the updated face recognition result information as the face recognition result information.

[0091] Sixthly, the execution body can send the face recognition result information to a face recognition terminal corresponding to the face recognition request information.

[0092] The technical solutions and related contents thereof serve as one of the inventive points of the embodiments of the present disclosure, and solve the third technical problem mentioned in the background. The third technical problem is that the obtained face recognition result involves sensitive information of a user, such as identity information and images, and directly returning the obtained face recognition result to a face recognition terminal leads to leakage of sensitive information of the user and low security of user information. Factors that lead to leakage of sensitive information of the user and low security of user information are usually as follows: the obtained face recognition result involves sensitive information of a user, such as identity information and images, and directly returning the obtained face recognition result to a face recognition terminal leads to leakage of sensitive information of the user and low security of user information. If the above factors are solved, the effect of reducing leakage of sensitive information of the user and improving security of user information can be achieved. To achieve this effect, the face recognition method of some embodiments of the present disclosure first performs face detection processing on a target user face image included in the face recognition result information, and obtains a face region image. In this way, the face region image can be obtained, and thus can be used to generate a blurred face image. Secondly, the face region image is subjected to blurring processing, and a blurred face image is obtained. In this way, the blurred face image can be obtained, and thus the security of the target user face image can be improved. Then, character detection processing is performed on target user identity information included in the face recognition result information, and a to-be-encrypted character group is obtained. In this way, each character that needs to be protected in the target user identity information can be obtained, and thus can be used to generate encrypted identity information. Subsequently, each to-be-encrypted character in the to-be-encrypted character group included in the target user identity information is subjected to encryption processing, and encrypted identity information is obtained. In this way, the encrypted target user identity information can be obtained, and thus the security of the target user identity information can be improved. Next, the face recognition result information is updated according to the blurred face image and the encrypted identity information, and updated face recognition result information is obtained as the face recognition result information. In this way, the encrypted face recognition result information can be obtained, and thus can be used to return to the face recognition terminal. Finally, the face recognition result information is sent to the face recognition terminal corresponding to the face recognition request information. In this way, the face recognition terminal can obtain the face recognition result information subjected to encryption processing, and thus the security of user information can be improved. Because the information involving user privacy in the face recognition result information is subjected to encryption processing when the face recognition result information is sent to the face recognition terminal corresponding to the face recognition request information, leakage of sensitive information of the user is reduced, and thus leakage of sensitive information of the user can be reduced and the security of user information can be improved.

[0093] Optionally, the execution subject can further perform the following steps:

[0094] In a first step, the face recognition type, the application platform identifier, the current time, the target face recognition interface information, and the face recognition result information included in the face recognition request information are determined as the call log information.

[0095] In a second step, the call log information is stored in a preset log information database. The preset log information database can be a database preset for storing call log information.

[0096] The above embodiments of the present disclosure have the following beneficial effects: the face recognition method of some embodiments of the present disclosure can reduce the number of repeated interface connections and shorten the time consumed. Specifically, the reason for the large number of repeated interface connections and the long time consumed is that the specifications and standards of different face recognition interfaces are not the same. When the number of face recognition requests is high and concurrent, the upper limit value of the face recognition interface second query rate cannot be uniformly set, resulting in a large number of repeated interface connections and a long time consumed. Based on this, the face recognition method of some embodiments of the present disclosure first determines, in response to receiving face recognition request information, a preset face recognition interface type corresponding to the face recognition type included in the face recognition request information as a target face recognition interface type. The face recognition request information includes a target user face image. Thus, the type of the interface for face recognition of the target user face image can be obtained. Second, the preset interface configuration information corresponding to the target face recognition interface type is determined as target interface configuration information. The target interface configuration information includes a face recognition interface information sequence. The face recognition interface information sequence corresponds to each face recognition interface. Thus, different face recognition interfaces for the same face recognition type can be uniformly set, thereby reducing the number of repeated interface connections. Then, each face recognition interface is detected to obtain a detection result information group. Thus, it can be determined whether each face recognition interface can be normally called. Subsequently, the target face recognition interface information is generated according to the detection result information group and the face recognition interface information sequence. Thus, the information of the face recognition interface for face recognition of the target user face image can be obtained, thereby enabling face recognition. Next, the face recognition result information is generated according to the target face recognition interface information and the target user face image. Thus, the face recognition operation can be completed. Finally, the face recognition result information is sent to a face recognition terminal corresponding to the face recognition request information. Thus, the face recognition terminal can obtain the face recognition result, and perform related operations according to the face recognition result information. Also, because the different face recognition interfaces corresponding to the same face recognition type are uniformly set when performing face recognition, the number of repeated interface connections is reduced, thereby reducing the number of repeated interface connections and shortening the time consumed.

[0097] Further referring to Figure 2 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a face recognition device, which device embodiments correspond to those method embodiments, and the device specifically can be applied in various electronic devices. Figure 1

[0098] As Figure 2 shown, the face recognition device 200 of some embodiments includes a first determining unit 201, a second determining unit 202, a detecting unit 203, a first generating unit 204, a second generating unit 205, and a sending unit 206. The first determining unit 201 is configured to determine, in response to receiving face recognition request information, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information as a target face recognition interface type, wherein the face recognition request information includes a target user face image; the second determining unit 202 is configured to determine preset interface configuration information corresponding to the target face recognition interface type as target interface configuration information, wherein the target interface configuration information includes a face recognition interface information sequence, and the face recognition interface information sequence corresponds to each face recognition interface; the detecting unit 203 is configured to perform detection processing on each face recognition interface to obtain a detection result information group; the first generating unit 204 is configured to generate target face recognition interface information according to the detection result information group and the face recognition interface information sequence; the second generating unit 205 is configured to generate face recognition result information according to the target face recognition interface information and the target user face image; and the sending unit 206 is configured to send the face recognition result information to a face recognition terminal corresponding to the face recognition request information.

[0099] It can be understood that the units recorded in the face recognition device 200 correspond to each step in the method described with reference to Figure 1 . Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, which will not be described here.

[0100] Reference is made below to Figure 3 , which shows a structural schematic diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the function and use range of the embodiments of the present disclosure.

[0101] As Figure 3 shown, the face recognition device 200 of some embodiments includes a first determining unit 201, a second determining unit 202, a detecting unit 203, a first generating unit 204, a second generating unit 205, and a sending unit 206. The first determining unit 201 is configured to determine, in response to receiving face recognition request information, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information as a target face recognition interface type, wherein the face recognition request information includes a target user face image; the second determining unit 202 is configured to determine preset interface configuration information corresponding to the target face recognition interface type as target interface configuration information, wherein the target interface configuration information includes a face recognition interface information sequence, and the face recognition interface information sequence corresponds to each face recognition interface; the detecting unit 203 is configured to perform detection processing on each face recognition interface to obtain a detection result information group; the first generating unit 204 is configured to generate target face recognition interface information according to the detection result information group and the face recognition interface information sequence; the second generating unit 205 is configured to generate face recognition result information according to the target face recognition interface information and the target user face image; and the sending unit 206 is configured to send the face recognition result information to a face recognition terminal corresponding to the face recognition request information.As shown, the electronic device 300 can include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301 that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or loaded into a random access memory (RAM) 303 from a storage device 308. Various programs and table data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0102] Generally, the following devices can be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 can allow the electronic device 300 to communicate wirelessly or wiredly with other devices to exchange table data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that not all of the shown devices are required to be implemented or present. More or fewer devices can alternatively be implemented or present. Figure 3 Each block shown in the flowcharts can represent a device or multiple devices as needed.

[0103] In particular, processes described above with reference to the flowcharts can be implemented as a computer software program according to some embodiments of the present disclosure. For example, some embodiments of the present disclosure include a computer program product including a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In some such embodiments, the computer program can be downloaded and installed from a network through the communication devices 309, or installed from the storage devices 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-described functions defined in the methods of some embodiments of the present disclosure are performed.

[0104] Note that the computer-readable medium in some embodiments of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having 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 thereof. In some embodiments of the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus or device. In some embodiments of the present disclosure, the computer-readable signal medium can include a program code carried in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated signal can take many forms, including but not limited to, an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium that can transmit, propagate or transport program for use by or in connection with an instruction execution system, apparatus or device. The program code contained in the computer-readable medium can be transmitted by any suitable medium, including but not limited to, a wire, cable, RF (radio frequency), or the like, or any suitable combination thereof.

[0105] In some embodiments, the client, server can communicate using any currently known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

[0106] The computer readable medium can be included in the electronic device, or can exist separately from the electronic device. The computer readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: in response to receiving face recognition request information, determine, as a target face recognition interface type, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information, wherein the face recognition request information includes a target user face image; determine, as target interface configuration information, preset interface configuration information corresponding to the target face recognition interface type, wherein the target interface configuration information includes a face recognition interface information sequence, and the face recognition interface information sequence corresponds to each face recognition interface; perform detection processing on the each face recognition interface to obtain a detection result information set; generate target face recognition interface information according to the detection result information set and the face recognition interface information sequence; generate face recognition result information according to the target face recognition interface information and the target user face image; and send the face recognition result information to a face recognition terminal corresponding to the face recognition request information.

[0107] Computer program code for carrying out operations of some embodiments of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0108] The flow and block diagrams in the drawings represent possible architectural, functional, and operational architectures of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow and block diagrams can represent a module, a segment, or a portion of code that comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or combinations of hardware and software.

[0109] The units described in some embodiments of the present disclosure can be implemented by means of software, or can be implemented by hardware. The described units can also be arranged in a processor, for example, a processor can be described as: a processor comprising a first determination unit, a second determination unit, a detection unit, a first generation unit, a second generation unit, and a sending unit. Among them, the name of these units does not constitute a limitation to the unit itself in some cases, for example, the first determination unit can also be described as: a unit that "in response to receiving face recognition request information, determines a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information in a preset face recognition interface type set as a target face recognition interface type, wherein the face recognition request information includes a target user face image".

[0110] The functions described above in the present document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, example types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0111] The above description is merely some of the preferred embodiments of the present disclosure and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or equivalent features thereof without departing from the above inventive concept. For example, the above features are replaced with technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) to form technical solutions.

Claims

1. A face recognition method, comprising: determining, in response to receiving face recognition request information, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information as a target face recognition interface type, wherein the face recognition request information comprises a target user face image; determining preset interface configuration information corresponding to the target face recognition interface type as target interface configuration information, wherein the target interface configuration information comprises a face recognition interface information sequence corresponding to each face recognition interface; performing detection processing on the each face recognition interface to obtain a detection result information group; generating target face recognition interface information according to the detection result information group and the face recognition interface information sequence; generating face recognition result information according to the target face recognition interface information and the target user face image; sending the face recognition result information to a face recognition terminal corresponding to the face recognition request information; wherein the generating target face recognition interface information according to the detection result information group and the face recognition interface information sequence comprises: for each detection result information in the detection result information group, determining face recognition interface information corresponding to the detection result information as abnormal face recognition interface information in response to determining that the detection result information satisfies a preset abnormal operation condition; deleting each face recognition interface information in the face recognition interface information sequence corresponding to each determined abnormal face recognition interface information to obtain a normal operation face recognition interface information sequence; generating target face recognition interface information according to the normal operation face recognition interface information sequence comprises: determining an initial value; for the normal operation face recognition interface information sequence and the initial value, performing the following determination steps: determining a normal operation face recognition interface information in the normal operation face recognition interface information sequence with an initial value as a first normal operation face recognition interface information; determining a face recognition interface corresponding to the first normal operation face recognition interface information as a first face recognition interface; determining second query rate threshold information corresponding to the first face recognition interface; determining a number of pending requests corresponding to the first face recognition interface; determining whether the first normal operation face recognition interface information satisfies a preset interface condition according to the second query rate threshold information and the number of pending requests; in response to determining that the first normal operation face recognition interface information satisfies the preset interface condition, determining the first normal operation face recognition interface information as the target face recognition interface information.

2. The method of claim 1, wherein, the face recognition request information further comprises an application platform identifier; and the method further comprises: determining a face recognition type, an application platform identifier, a current time, the target face recognition interface information, and the face recognition result information included in the face recognition request information as a call log information; storing the call log information to a preset log information database.

3. The method of claim 1, wherein, the determination steps further comprise: In response to determining that the first running normal face recognition interface information does not meet the preset interface condition, incrementally processing the initial value, and continuing to execute the above determination step.

4. The method of claim 2, wherein, The preset face recognition interface type corresponding to the face recognition type included in the face recognition request information in the preset face recognition interface type set is determined as a target face recognition interface type, including: Determining the platform permission type corresponding to the application platform identifier included in the face recognition request information; In response to determining that the platform permission type meets the preset permission condition, determining the preset face recognition interface type corresponding to the face recognition type included in the face recognition request information in the preset face recognition interface type set as the target face recognition interface type.

5. The method of claim 1, wherein, The preset face recognition interface type corresponding to the face recognition type included in the face recognition request information in the preset face recognition interface type set is determined as a target face recognition interface type, including: Face detection processing is performed on the target user face image to obtain a face region image; Determining the face size of the face region image; Determining the face sharpness of the face region image; Five-wink detection processing is performed on the face region image to obtain a five-wink detection image set; According to the five-wink detection image set, the face integrity is determined; According to the face size, the face sharpness and the face integrity, the face quality score information is generated; In response to determining that the face quality score information meets the preset score condition, the preset face recognition interface type corresponding to the face recognition type included in the face recognition request information in the preset face recognition interface type set is determined as the target face recognition interface type.

6. A face recognition device, comprising: A first determination unit configured to determine, in response to receiving face recognition request information, a preset face recognition interface type corresponding to a face recognition type included in the face recognition request information in a preset face recognition interface type set as a target face recognition interface type, wherein the face recognition request information includes a target user face image; A second determination unit configured to determine preset interface configuration information corresponding to the target face recognition interface type as target interface configuration information, wherein the target interface configuration information includes a face recognition interface information sequence, and the face recognition interface information sequence corresponds to each face recognition interface; A detection unit configured to perform detection processing on the each face recognition interface to obtain a detection result information group; A first generation unit configured to generate target face recognition interface information according to the detection result information group and the face recognition interface information sequence; A second generation unit configured to generate face recognition result information according to the target face recognition interface information and the target user face image; A sending unit configured to send the face recognition result information to a face recognition terminal corresponding to the face recognition request information; The first generation unit is configured to: For each detection result information in the detection result information set, in response to determining that the detection result information meets a preset abnormal operation condition, face recognition interface information corresponding to the detection result information is determined as abnormal face recognition interface information; Each face recognition interface information corresponding to each abnormal face recognition interface information in the face recognition interface information sequence is deleted, to obtain a normal operation face recognition interface information sequence; According to the normal operation face recognition interface information sequence, target face recognition interface information is generated, including: Determining an initial value; For the normal operation face recognition interface information sequence and the initial value, the following determination steps are performed: The normal operation face recognition interface information in the normal operation face recognition interface information sequence is determined as the first normal operation face recognition interface information. The face recognition interface corresponding to the first normal operation face recognition interface information is determined as the first face recognition interface. Determine the second query rate threshold information corresponding to the first face recognition interface; Determine the number of pending requests corresponding to the first face recognition interface; According to the second query rate threshold information and the number of pending requests, it is determined whether the first normal operation face recognition interface information meets a preset interface condition. In response to determining that the first normal operation face recognition interface information meets the preset interface condition, the first normal operation face recognition interface information is determined as the target face recognition interface information.

7. An electronic device, comprising: one or more processors; a storage device having stored thereon one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-5.

8. A computer readable medium having stored thereon a computer program, wherein, The computer program is executed by the processor to implement the method of any one of claims 1-5. The computer program is executed by the processor to implement the method of any one of claims 1-5.

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