WO2021000829A1

WO2021000829A1 - Multi-dimensional identity information identification method and apparatus, computer device and storage medium

Info

Publication number: WO2021000829A1
Application number: PCT/CN2020/098801
Authority: WO
Inventors: 方成银
Original assignee: 平安科技（深圳）有限公司
Priority date: 2019-07-03
Filing date: 2020-06-29
Publication date: 2021-01-07
Also published as: CN110443137A; CN110443137B

Abstract

The present application relates to the technical field of artificial intelligence. Provided are a multi-dimensional identity information identification method and apparatus, a computer device and a storage medium. The method comprises: during the tracking and identification of a target object in a video, after preset multi-dimensional identity information to be identified of the target object is acquired, identifying identity information of an object to be identified in the video according to the preset multi-dimensional identity information to be identified and by means of a target detection algorithm, so as to obtain a corresponding identification probability of said preset multi-dimensional identity information with respect to the object to be identified; and finally, determining, according to the identification probability, whether the object to be identified matches the target object. According to the present application, where a facial recognition condition is not ideal, the object to be identified is quickly distinguished by means of the multi-dimensional identity information to be identified, which information is relatively easily obtained, and different types of identity information to be identified are correspondingly distinguished by means of weights, such that the identification speed and precision in a complex scenario are improved.

Description

Multi-dimensional identity information identification method, device, computer equipment and storage medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on July 3, 2019, the application number is 201910601411.9, and the invention title is "Multi-dimensional identification information identification methods, devices, computer equipment and storage media", and its entire contents Incorporated in this application by reference.

Technical field

This application relates to the field of artificial intelligence technology, in particular to a multi-dimensional identity information recognition method, device, computer equipment and storage medium.

Background technique

The existing applications for target recognition and capture in video basically use the method of face recognition to track the target object. The requirements for scenes such as the posture lighting of the face in the video are high, and the clarity of the video is also required. Very high requirements, the inventor realized that in the case of unsatisfactory recognition conditions, due to the small difference in facial features, the equipment requirements are high, and the misrecognition rate is high, which is not conducive to use in complex scenarios, and the recognition speed is not enough fast.

technical problem

The main purpose of this application is to provide a multi-dimensional identification information identification method, device, computer equipment and storage medium, which can quickly identify the identity of the object to be identified in the video and improve the target identification speed in complex scenarios.

Technical solutions

This application proposes a multi-dimensional identity information identification method, including:

According to the target detection algorithm, the identification information of the object to be identified in the video is identified, and the identification probability of the object to be identified in all categories of the preset multi-dimensional identity information to be identified is obtained; where the preset multi-dimensional identity information to be identified includes N categories of first identity information to be identified in the human body feature dimension and M categories of second identity information to be identified in the clothing dimension; preset multi-dimensional identity information to be identified as the identity information corresponding to the target object, and both M and N Positive integer;

Look up the corresponding first determination result in the first preset determination table according to the recognition probability to determine whether the corresponding type of preset multi-dimensional identity information to be recognized is recognized according to the first determination result; in the first preset determination table, If the types of multi-dimensional identity information to be identified are different, the judgment conditions corresponding to the identification probabilities are not completely the same;

Perform statistics on multiple first determination results according to preset statistical rules, and look up the corresponding second determination results in the second preset determination table according to the statistical results to determine whether the object to be identified matches the target object; in the second The second judgment result corresponding to different statistical results is preset in the preset judgment table.

This application also proposes a multi-dimensional identity information recognition device, including:

The recognition module is used to identify the identity information of the object to be identified in the video according to the target detection algorithm, and obtain the corresponding identification probability of the object to be identified in all categories of preset multi-dimensional identity information to be identified; wherein, the preset to be identified The multi-dimensional identity information includes N categories of first identity information to be identified in the human body feature dimension and M categories of second identity information to be identified in the clothing dimension; the preset multi-dimensional identity information to be identified is the identity information corresponding to the target object, Both M and N are positive integers;

The first search module is configured to search for the corresponding first determination result in the first preset determination table according to the recognition probability, so as to determine whether the corresponding type of preset multi-dimensional identity information to be recognized is recognized according to the first determination result; A predetermined judgment table presets the judgment conditions of the recognition probability, where different types of preset multi-dimensional identity information to be recognized have different types, and the judgment conditions corresponding to the recognition probabilities are not completely the same;

The second search module is used to perform statistics on a plurality of first determination results according to preset statistical rules, and search for corresponding second determination results in the second preset determination table according to the statistical results to determine whether the object to be identified is the target The objects match; second judgment results corresponding to different statistical results are preset in the second preset judgment table.

This application also proposes a computer device, including a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of a multi-dimensional identity information identification method:

This application also proposes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of a multi-dimensional identity information identification method are realized:

Beneficial effect

Compared with the prior art, this application has the beneficial effect that: this application proposes a multi-dimensional identity information recognition method, device, computer equipment and storage medium. When tracking and recognizing a target object in a video, the After preset the multi-dimensional identity information to be identified, the identity information of the object to be identified in the video is identified according to the preset multi-dimensional identity information to be identified and the target detection algorithm is used to obtain the preset multi-dimensional identity information to be identified. According to the corresponding recognition probability on the object, it is finally determined whether the object to be recognized matches the target object according to the recognition probability. In the case of unsatisfactory face recognition conditions, this application uses relatively easy-to-obtain multi-dimensional identity information to be identified to quickly identify objects to be identified, and distinguishes the corresponding weights of different types of identity information to be identified, which improves Recognition speed and accuracy in complex scenes.

Description of the drawings

FIG. 1 is a schematic diagram of the steps of a multi-dimensional identity information identification method in an embodiment of this application;

2 is a schematic diagram of modules of a multi-dimensional identity information identification device in an embodiment of the application;

FIG. 3 is a schematic block diagram of modules of a computer device in an embodiment of the application;

FIG. 4 is a schematic block diagram of modules of a storage medium in an embodiment of the application.

The best mode of the invention

It should be understood that the specific embodiments described here are only used to explain the application, and are not used to limit the application.

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the 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 the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of this application.

It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the difference between components in a specific posture (as shown in the accompanying drawings). The relative positional relationship, movement situation, etc., if the specific posture changes, the directional indication changes accordingly, and the connection can be a direct connection or an indirect connection.

In addition, the descriptions related to "first", "second", etc. in this application are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on what can be achieved by a person of ordinary skill in the art. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that such a combination of technical solutions does not exist. , Not within the scope of protection required by this application.

1, in an embodiment of the present application, a multi-dimensional identity information identification method is proposed, which includes the following steps:

S1: According to the target detection algorithm, the identification information of the object to be identified in the video is identified, and the identification probability of the object to be identified in all categories of the preset multi-dimensional identity information to be identified is obtained; among them, the multi-dimensional identity to be identified is preset The information includes N categories of first identification information to be identified in the human body feature dimension and M categories of second identification information to be identified in the clothing dimension; the preset multi-dimensional identification information to be identified is the identity information corresponding to the target object, M and N All are positive integers;

S2: Search for the corresponding first determination result in the first preset determination table according to the recognition probability to determine whether the corresponding category of identity information to be recognized is recognized according to the first determination result; in the first preset determination table, preset If the types of multi-dimensional identity information to be identified are different, the judgment conditions corresponding to the identification probability are not completely the same;

S3: Perform statistics on multiple first determination results according to preset statistical rules, and look up the corresponding second determination results in the second preset determination table according to the statistical results to determine whether the object to be identified matches the target object; The second judgment results corresponding to different statistical results are preset in the second preset judgment table.

When the above steps are implemented, the method of this application does not directly recognize the face of the object to be identified when identifying the object to be identified in the video to be identified, but first uses the obtained preset multi-dimensional identity information to be identified as An identity information of the target object in order to identify the target object, and then the target detection algorithm is used to identify the object to be identified, and the identification probabilities corresponding to the identification information of the object to be identified in different dimensions are obtained. The obtained recognition probability is searched for the corresponding determination result in the preset determination table to determine whether the object to be identified matches the acquired identity tag, that is, to determine whether the object to be identified is the target object. Among them, the multi-dimensional identity information to be identified mainly includes the human body characteristic dimension and the clothing dimension. The human body characteristic dimension refers to the characteristic information of the human body itself, which includes multiple different types of identity information to be identified, such as gender and age. , Body shape, hair, etc.; while the clothing dimension refers to the clothing of the target object, which also includes a number of different types of identification information to be identified, such as one or more of hats, glasses, tops, pants and shoes .

In a specific embodiment, for example, the acquired multi-dimensional identity information to be identified is "male, 35 years old, short hair, red hat, blue shirt, black pants", where "male, 35 years old, short hair" is a human body feature There are three different types of identification information to be identified in the dimension. "Red hat, blue top, and black pants" are the three different types of identification information to be identified in the clothing dimension. The object to be identified in the video is performed according to the target detection algorithm. The identification and screening of the multi-dimensional identity information to be identified can obtain the identification probabilities of different types of identity information to be identified in different dimensions of the object to be identified.

For example, for the gender detection of the object to be recognized, the gender recognition algorithm based on eigenface, the gender recognition method based on Fisher criterion, and the face based on Adaboost (Adaptive Boosting) + SVM (support vector machine) can be used. Gender classification algorithm, etc., to determine whether the gender of the object to be identified in the video is consistent with the acquired identity information to be identified.

For the age detection of the object to be recognized, a face age estimation algorithm that combines LBP (local binarization mode) and HOG (gradient histogram) features can be used to extract the local statistical features of faces that are closely related to age changes, and use CCA The method of (canonical correlation analysis) is integrated, and finally the age is estimated by the method of SVR (Support Vector Machine Regression) to determine whether the age of the object to be identified in the video is consistent with the obtained identity information to be identified. Because accurate age recognition has high requirements on image quality, and the error rate may be large, it is not conducive to rapid screening of the objects to be recognized in the video to be recognized. Therefore, the recognition of age can be divided into age groups, such as "children", "Juvenile", "youth", "middle-aged" and "aged", etc., transform the regression problem of accurate age recognition into the classification problem of age recognition, improve the recognition speed and reduce the recognition error rate.

For the clothing color detection of the object to be recognized, the RGB model or the HSV model can be used for recognition. For example, the image to be recognized can be converted into an HSV model through cvtColor(imgOriginal, imgHSV, COLOR_BGR2HSV); Then perform color detection, such as using void inRange(InputArray src, InputArray lowerb, InputArray upperb, OutputArray dst); function for color detection, the function of this function is to detect whether each pixel of the src image is between lowerb and upperb, if it is, this pixel is set to 255 , And save it in the dst image, otherwise it is 0; the binary image of the target color can be obtained through the function, and then the binary image is opened to remove the noise, and then the closed operation is used to connect the connected domains. According to the obtained value The specific color of the clothing of the object to be identified can be detected, so as to determine whether the clothing color of the object to be identified in the video is consistent with the acquired identity information to be identified. Further, the texture of the image can also be detected to learn the material of the clothes on the object to be identified.

In the above target detection algorithm, the output of the vector machine according to the detection algorithm actually belongs to the classification probability of the object to be recognized, and all are a number. For example, in gender recognition, it belongs to "male" or "female". For two classification problems, the result obtained by the detection algorithm during detection is actually the recognition probability. For example, the recognition probability of a "male" object to be recognized is "0.78", and the recognition probability of a female is "0.22", then the output result is (male , 0.78; Female, 0.22), so it is determined that the gender of the identified object is male; for another example, the recognition probability of the top color of "red" is "0.6", the recognition probability of "yellow" is "0.1", and it is "blue" The recognition probability of "color" is "0.08", the recognition probability of "green" is "0.12", and the recognition probability of "orange" is "0.1", then the output result is (red, 0.6; yellow, 0.1; blue, 0.08; green, 0.12; yellow, 0.1), so it is determined that the color of the shirt with the identification object is red. In this way, various dimensions of the identification information to be identified are identified for the objects to be identified in the video, and the corresponding identification probabilities of the identification information to be identified in each category are obtained.

After obtaining the corresponding identification probabilities of different types of identification information to be identified, the corresponding first determination result is searched in the first preset determination table according to the identification probability to determine whether the multi-dimensional identification information to be identified is recognized. It is assumed that the judgment condition of the recognition probability is preset in the judgment table, wherein the judgment conditions corresponding to different types of identity information to be identified are different. In a specific embodiment, for example, the identification information to be identified is "male, 35 years old, short hair, red hat, blue top, black pants", and statistics are obtained to obtain the identification probability of an object to be identified in different categories. They are "male, 0.85", "youth, 0.78", "red hat, 0.7", "blue shirt, 0.4", "black pants, 0.68", and the recognition probabilities obtained are set in the first preset Search in the judgment list. For example, in the preset judgment list, the preset probability threshold for gender is 0.8, that is, if the recognition probability of gender is greater than or equal to 0.8, the first judgment result is that the to-be-recognized The gender of the subject is male; the preset probability threshold for age is 0.65, that is, if the recognition probability of a young age is greater than or equal to 0.65, the first judgment result is the youth of the subject to be identified according to the judgment condition, and other dimensions are different The same is true for categories of identification information to be identified.

After finding the corresponding first judgment result in the first preset judgment list according to the identification probability of each category of identity information to be identified, the multiple first judgment results are counted through the preset statistical rules, and according to the statistical results in the first Second, look up the corresponding second determination result in the preset determination table to determine whether the object to be identified matches the target object. The second judgment results corresponding to different statistical results are preset in the second preset judgment table, and the second judgment results include three different results: "matching", "not matching" and "uncertain". In a specific embodiment, the preset statistical rule only counts the number of "yes" or "no" in the first determination result, and the specific value of each recognition probability. If the first determination result is the category that recognizes the category If the identity information to be identified is "Yes", if the category of identity information to be identified is not recognized, it is "No". According to the number of the first judgment results in the statistical results as "Yes" and "No", Look up the corresponding second determination result in the second preset determination table. In a specific embodiment, for example, only after the first determination result of all categories of identity information to be identified has passed, that is, "Yes", the determination The identity tag of the target object is recognized, the corresponding second determination result is found in the second preset determination table as "match", and it is determined that the object to be recognized matches the target object. In another specific embodiment, for example, there are five different types of identification information to be identified. Based on the consideration that there is a certain error in recognition, if the first determination results of the four different types of identification information to be identified are all "yes" , It is determined that the object to be identified matches the target object; further, other requirements can be made for the identification information to be identified that fails the final judgment result, such as limiting its identification probability to be between "0.3-0.7" It can serve as an auxiliary reference under the premise that the first judgment results of the other four different types of identity information to be identified are passed; for example, it can be set to find the corresponding in the second preset judgment table. The second determination result of is "undefined", and the user can mark the object to be identified as a suspected target object according to the second determination result.

In a preferred embodiment, the identification information of the object to be identified in the video is identified according to the target detection algorithm, and the identification probability of the object to be identified in all categories of preset multi-dimensional identity information to be identified is obtained. Step S1 ,include:

S11: Separate the object to be recognized from the background of the video to be recognized by an image segmentation algorithm;

S12: Perform part key point detection on the separated object to be recognized, and segment the recognition area of the object to be recognized according to the key point of the part, and the recognition area includes the head, upper body, and lower body;

S13: Identify the to-be-identified identity information of each corresponding category in the identification area, and obtain the identification probability.

In the implementation of the above steps, when performing the corresponding identification and detection of the object to be identified in the video to be identified, the object to be identified is first separated from the background of the video to be identified, that is, only the object to be identified is detected, and the object to be identified is not detected. The video background is detected to reduce the amount of detection operations. Specifically, an image segmentation algorithm can be used to separate the object to be recognized from the background of the video to be recognized, such as fixed threshold segmentation based on threshold segmentation, histogram bimodal method, OTSU method (maximum between-class variance method), etc.; Various edge detection operators of edge detection methods; region growing method, split merging method and watershed segmentation method based on region segmentation; Normalized Cuts algorithm based on graph theory segmentation, Graph Cuts algorithm, Superpixel lattice algorithm, etc.; based on energy functional The segmentation method, wavelet-based segmentation method and neural network-based segmentation method, etc.

After the object to be recognized is separated from the video background, the key points of the separated object to be recognized are detected and the recognition area is segmented. The recognition area includes the head, upper body and lower body, such as the neck between the head and upper body. It is a key point of a part. The waist between the upper body and the lower body is also a key point of the part. The hands, elbows, shoulders, chest, face, etc. are also key points of the part. The key points of multiple parts are detected by the object to be recognized. Construct, divide the object to be recognized into three recognition areas, including the head, upper body and lower body, so as to subsequently identify the identity information to be recognized in the corresponding dimensions in each recognition area. In a specific embodiment, a haar cascade can be used to detect the head, upper body, and lower body of the object to be identified.

After dividing the object to be identified into different identification areas, identify the corresponding type of identity information to be identified in the identification area, and obtain the identification probability. In a specific embodiment, when recognizing the identity information to be recognized in the clothing dimension of "red hat", first confirm that the recognition area is the head area of the object to be recognized, and then check whether the head of the object to be recognized is If you wear a hat, check whether the hat is red, so as to get the recognition probability of "red hat". In one embodiment, if the recognition probability of recognizing "wearing a hat" is 0.9, and the probability of recognizing "red" is 0.8, then the recognition probability of the corresponding "red hat" is 0.9×0.8=0.72. In another embodiment, if the recognition probability of "wearing a hat" is greater than or equal to 0.9, then it can be directly determined that the recognition probability of the corresponding "red hat" is that the probability of recognizing "red" is 0.8. The probability calculation is specially limited. The same is true for the recognition of black pants in other recognition areas, such as the recognition area of the lower body, and there is no special restriction when calculating the recognition probability.

In a preferred embodiment, the corresponding first determination result is searched in the first preset determination table according to the recognition probability, so as to determine whether the corresponding type of preset multi-dimensional identity information to be recognized is recognized according to the first determination result. Step S2 includes:

S21: Search for a corresponding determination condition in the first preset determination table according to the corresponding category of the recognition probability; the determination condition includes the preset probability threshold of the recognition probability in the corresponding category;

S22: Compare the recognition probability with a preset probability threshold; the preset probability threshold includes a first preset probability threshold and a second preset probability threshold;

S23: If the recognition probability is higher than the first preset probability threshold, it is determined according to the first determination result that the identity information to be recognized of the corresponding category is recognized;

S24: If the recognition probability is higher than the second preset probability threshold and lower than the first preset probability threshold, it is determined whether it is uncertain whether the corresponding category of identity information to be recognized is recognized according to the first determination result;

S25: If the recognition probability is lower than the second preset probability threshold, it is determined according to the first determination result that the identity information to be recognized of the corresponding category is not recognized.

In the implementation of the above steps, since in the first preset determination table, the types of the multi-dimensional identity information to be recognized are preset to be different, the determination conditions corresponding to the recognition probability are not completely the same, so the corresponding category according to the recognition probability is first A corresponding determination condition is searched in a predetermined determination table to determine how to determine the recognition probability of a given category, and the determination condition includes a predetermined probability threshold of the recognition probability in the corresponding category.

Then the recognition probability is compared with the found preset probability threshold. If it is higher than the first preset probability threshold, it is determined that the identification information of the category to be identified is recognized; if it is higher than the second preset probability threshold and lower than If the first preset probability threshold is not recognized, it is determined whether the category of identity information to be recognized is uncertain; if it is lower than the second preset probability threshold, it is determined that the category of identity information to be recognized is not recognized. In a specific embodiment, the type of identity information to be identified is different, and the corresponding preset probability threshold will also be different. For example, when performing age recognition on the object to be identified, relatively clear facial images are required for age recognition. , The image quality requirements are high, and the error rate is relatively large, the first preset probability threshold can be set lower, for example, 0.7; while the object to be identified is identified by long shorts or whether there is a person wearing glasses During recognition, due to the large difference between trousers and shorts, and the difference between wearing glasses and not wearing glasses, and the requirements for image quality during detection are relatively low, the first preset probability threshold can be set Higher, for example 0.9. By assigning different preset probability thresholds to different types of identification information to be identified, the preset probability threshold is lower if the error rate is higher, and the preset probability threshold is higher if the error rate is low, so that the identified object can be treated as quickly as possible Make a judgment to determine whether it is consistent with the acquired identity information to be identified, while minimizing detection errors.

Further, the first judgment result is divided into three categories: the identification information to be identified, the identification information to be identified is uncertain, and the identification information to be identified is not identified, instead of the simple binary processing of yes or no, so that It can be handled more flexibly when making comprehensive judgments. For example, for example, there are five different types of identification information to be identified. Based on the consideration of certain errors in recognition, after the first determination result of the four different types of identification information to be identified is that the corresponding category of identification information to be identified is recognized, It is determined that the object to be identified is consistent with the target object; further, other requirements can be made for the remaining identity information for which the first judgment result is not passed, such as limiting the first judgment result to be uncertain or not The identification of the identity information to be identified in the corresponding category can serve as an auxiliary reference under the premise that the first determination result of the other four different categories of identity information to be identified is the identification of the identity information to be identified in the corresponding category. With this setting, when the image quality is not so ideal, certain categories of identity information that are difficult to identify and have a large detection error rate can still serve as an auxiliary reference, not just as an unidentified corresponding category The first determination result of the to-be-identified identity information is rejected, it is possible to make an accurate determination as quickly as possible on the object to be identified, to determine whether it matches the acquired multi-dimensional identity information to be identified, and to minimize detection errors.

In a preferred embodiment, the step S3 of performing statistics on the multiple first determination results according to a preset statistical rule includes:

S31: According to the preset statistical rules, if the identification information to be identified is determined according to the first determination result, the statistical score is assigned a value of 1; if it is determined that the identification information to be identified is uncertain according to the first determination result, then The statistical score is assigned a value of 0; if the identification information to be identified is not recognized according to the first judgment result, the statistical score is assigned a score of -1;

S32: Perform superposition calculation on the statistical scores of all the first judgment results to obtain statistical results.

In the implementation of the above steps, in order to make the first judgment result have a more intuitive judgment method, a corresponding statistical score is assigned to different first judgment results, such as score A, score B, and score C. In a specific embodiment, if the identification information to be identified is determined according to the first determination result, the statistical score is assigned a value of 1 (A); if it is determined according to the first determination result that it is uncertain whether the identity to be identified is identified Information, the statistical score is assigned a value of 0 (B); if the identification information to be identified is not recognized according to the first determination result, the statistical score is assigned a value of -1 (C). By assigning a corresponding digital statistical score to different first judgment results, the judgment result is more intuitive and easy to count.

After the statistical scores of all the first judgment results are obtained, the statistical scores of all the first judgment results are superimposed and calculated to obtain the statistical result. The statistical result is an intuitive score value. The higher the score value, the object to be identified The higher the match with the target audience. Then, according to the score value, the corresponding second determination result is searched in the second preset determination table to determine whether the object to be identified matches the target object. In a specific embodiment, in the second preset judgment table, a certain score threshold is preset, and when the statistical result is greater than the preset score threshold, the first object matching the target object is found. 2. Judgment results.

In a preferred embodiment, the step S32 of superimposing the statistical scores of all the first judgment results to obtain the statistical results includes:

S321: Obtain respective statistical weights of different types of identity information to be identified in the preset weight distribution table;

S322: Perform a superposition calculation on the statistical scores of all the first judgment results according to the first formula to obtain the statistical results; where the first formula is:

P=W1*A+W2*B+W3*C;

W1, W2, and W3 are the statistical weights corresponding to different types of preset multi-dimensional identity information to be identified.

In the implementation of the above steps, after obtaining the digital statistical scores corresponding to all the first judgment results, the statistical scores of all the first judgment results are superimposed according to the first formula to obtain the statistical results. Among them, different types of identification information are to be identified. The determination results have their corresponding statistical weights. In a specific embodiment, it is assumed that there are four different types of identification information to be identified as A1, A2, A3, and A4, and the statistical weights in the statistical results of the identification probability are defined as W1, W2, W3, W4, The corresponding statistical score of the first judgment result is: 1, 0, -1, 1, then the probability that the object to be identified matches the acquired identity information to be identified is P = W1 – W3 + W4, finally compare the probability P with the preset probability threshold. If it exceeds the preset probability threshold, the object to be identified is considered to be consistent with the acquired identity information, otherwise the object to be identified is considered to be the same The identity information does not match. Depending on the type of identity information to be identified, the corresponding statistical weights will be different. For example, when the object is to be identified for age recognition, because age recognition requires relatively clear facial images, the image quality requirements are high, and the error The rate is also relatively large, the statistical weight can be set lower, for example, 0.2; and when the object to be identified is recognized for long shorts or whether there is recognition of wearing glasses, due to the large difference between trousers and shorts , There is a big difference between wearing glasses and not wearing glasses, and the requirements for image quality during detection are relatively low, so the statistical weight can be set higher, for example, 0.5, a variety of different types of identity information to be identified The sum of its statistical weights is 1. By assigning different statistical weights to different types of identification information to be identified, the statistical weight is lower if the error rate is higher, and the statistical weight is higher if the error rate is low. When making statistical judgment results, focus on the lower error rate The identity information to be identified minimizes the impact of errors caused by the identity information to be identified with a high error rate, so that accurate judgments can be made as quickly as possible on the object to be identified, and whether it is consistent with the acquired identity information to be identified. At the same time minimize detection errors.

In a preferred embodiment, the step of obtaining the statistical weights corresponding to different types of preset to-be-identified multi-dimensional identity information in the preset weight distribution table includes:

S3211: Obtain pre-associated current environmental factors according to the preset categories of the multi-dimensional identity information to be identified. The current environmental factors include one or more of the current temperature, current air quality, current visibility, current geographic location, and height from the ground A combination of factors; among them, the current environmental factors pre-associated with different types of preset multi-dimensional identity information to be identified are not completely the same;

S3212: Determine the corresponding statistical weight in the preset weight distribution table according to the preset type of the multi-dimensional identity information to be identified and current environmental factors; set the corresponding preset weight distribution table in the category of the preset multi-dimensional identity information to be identified Multiple threshold ranges of environmental factors, and different threshold ranges of environmental factors correspond to different statistical weights.

When the above steps are implemented, in a specific embodiment, the device first obtains the pre-associated current environmental factors according to the preset type of the multi-dimensional identity information to be identified, and then according to the preset type of the multi-dimensional identity information to be identified and the current environment The factor determines the corresponding statistical weight in the preset weight distribution table. For example, when the device detects real-time video or detects certain specific videos, it can determine the statistical weights corresponding to different dimensions of identity information through the current environmental factors obtained. The current environmental factors include current temperature, current air quality, current visibility, A combination of one or more environmental factors in the current geographic location and height from the ground. When the device is performing detection and identification, it can obtain the current environmental factors through the network. The acquisition method can be the device actively search through the network, such as current environmental factors such as current temperature, current air quality, current geographic location, etc., or the device through the network Receive user input of current environmental factors. The device can also acquire current environmental factors through sensors. For example, when acquiring the current environmental factor of the height from the ground, the distance between the camera and the ground can be obtained through the measurement of the distance sensor on the camera. Among them, the current environmental factors pre-associated with different types of preset multi-dimensional identity information to be identified are not completely the same. For example, the length of pants is mainly related to the current temperature, so the preset multi-dimensional identity information of the type of pants length to be identified The pre-associated current environmental factors include the current temperature, and the pre-recognized multi-dimensional identity information of whether to wear glasses and hair length is not related to the current temperature, so the pre-associated current environmental factors do not include Current temperature; for example, the pre-recognized multi-dimensional identity information of the shoe color category has little correlation with current environmental factors such as current air quality, current visibility, and current geographic location, but it is related to the height from the ground. The current environmental factors have a large correlation, so the current environmental factors pre-associated include the height from the ground.

After obtaining the pre-associated current environmental factors, the corresponding statistical weights are determined in the preset weight distribution table according to the preset types of the multi-dimensional identity information to be identified and the current environmental factors; Multiple environmental factor threshold ranges are set to identify the categories of multi-dimensional identity information, and different environmental factor threshold ranges correspond to different statistical weights. For example, when the current temperature is 0°C, although the recognition error rate is low when the object to be identified is identified by long shorts, since when the temperature is 0°C, people basically wear long pants, and the screening significance is more significant. Therefore, when the identity information to be identified is trousers, its statistical weight is automatically reduced, for example, to 0.1; and when the current temperature is 0℃ and the identity information to be identified is shorts, there are basically few people at this time. Will wear shorts, shorts have a more significant screening significance, then its statistical weight will be automatically increased, for example, to 0.7, and when the current temperature is 30℃, the statistical weight ratio of trousers and shorts will be reversed. People who wear shorts are more common, while those who wear long trousers are relatively small. The statistical weight of trousers is higher, and the statistical weight of shorts is lower. As for whether there is identity information to be identified for wearing glasses, because of whether they wear glasses It has little relationship with the current temperature, so its statistical weight does not change due to changes in the current temperature. In another specific embodiment, for the "blue shoes" dimension of the identity information to be identified, since shoes generally occupy a relatively small proportion in the video, the image definition is generally poor, and the height of the video camera is generally relatively low. High, the shoes are not easy to shoot clearly, so the statistical weight of the recognition result of "blue shoes" is generally low, such as 0.1, but if the camera is relatively close to the ground, such as 1 meter, then In the video, a clearer shoe image can be obtained, and its statistical weight is automatically increased, for example, 0.5. The same is true for other types of current environmental factors. When the current environmental factors have a certain dimension of identification information to be identified When there is a positive impact, the statistical weight is automatically increased; when the current environmental factors have a negative impact on the identification of a certain dimension of identity information to be identified, its statistical weight is automatically reduced; when the current environmental factors affect the identity of a certain dimension to be identified When the identification of information has no impact, the statistical weight is determined according to the preset statistical weight or the statistical weight is evenly distributed. By obtaining the pre-associated current environmental factors and the preset threshold range in which they are located, the statistical weights corresponding to the identity information of different dimensions are determined, and the identification information with higher significance to be screened is given higher statistical weight, and the ones with lower significance are selected. The identification information uses a lower statistical weight to increase the screening speed as much as possible, so that an accurate judgment can be made as quickly as possible on the object to be identified, whether it is consistent with the acquired identification information to be identified, and the detection error is minimized.

In a preferred embodiment, the identification information of the object to be identified in the video is identified according to the target detection algorithm, and the identification probabilities corresponding to all categories of the preset multi-dimensional identity information to be identified are obtained respectively. Before step S1, it also includes:

S01: By receiving an input setting instruction of preset multi-dimensional identity information to be identified or performing identity information identification on a designated target object through a target detection algorithm, the preset multi-dimensional identity information to be identified is obtained.

In the implementation of the above steps, when detecting the video to be recognized, it is first necessary to determine the preset multi-dimensional identity information of the target object to be recognized. In a specific embodiment, when the user knows how to accurately input the setting instructions or there is no image of the target object for identification, the device can receive the user's input settings of the multi-dimensional identity information to be identified through wired or wireless communication. Instructions to determine the identification information to be detected. In another specific embodiment, when the user does not know how to enter the identity information to be identified in the corresponding dimension or the device cannot receive the entered identity information to be identified, the user can designate a specific identification object as the target object, and then the device can pass The target detection algorithm recognizes the identity information of the specific identification object to actively obtain the multi-dimensional identity information to be identified. In practical applications, the device detects and recognizes images specified by the user, such as detecting and recognizing a full-body photo of a specific recognized object, and obtains the gender, age, body type, and clothes of the specific recognized object as a pre-recognition. Suppose multi-dimensional identity information to be identified, and then perform detection and recognition in the video according to the identified preset multi-dimensional identity information to be identified, and find the object to be identified that matches the multi-dimensional identity information to be identified. Dimensional identity information is used to detect people in the video that match the specific identification object in the image.

2, this application also proposes a multi-dimensional identity information recognition device, including:

The recognition module 10 is used to identify the identity information of the object to be recognized in the video according to the target detection algorithm, and obtain the corresponding recognition probability of the object to be recognized in all categories of preset multi-dimensional identity information to be recognized; Identifying multi-dimensional identity information includes N categories of first identity information to be identified in the human body feature dimension and M categories of second identity information to be identified in the clothing dimension; preset the multi-dimensional identity information to be identified as the identity information corresponding to the target object , M and N are both positive integers;

The first search module 20 is configured to search for the corresponding first determination result in the first preset determination table according to the recognition probability, so as to determine whether the corresponding type of preset multi-dimensional identity information to be recognized is recognized according to the first determination result; In the first predetermined judgment table, the judgment conditions of the recognition probability are preset, wherein, if different types of preset multi-dimensional identity information to be identified have different types, the judgment conditions corresponding to the recognition probabilities are not completely the same;

The second search module 30 is configured to perform statistics on a plurality of first determination results according to preset statistical rules, and search for corresponding second determination results in the second preset determination table according to the statistical results, to determine whether the object to be identified is compatible with The target object matches; the second judgment result corresponding to different statistical results is preset in the second preset judgment table.

The operations performed by the aforementioned modules 10-30 respectively correspond to the steps of the multi-dimensional identity information identification method of the foregoing embodiment, and will not be repeated here.

Further, corresponding to the subdivision steps of the multi-dimensional identity information identification method of the foregoing embodiment, the aforementioned modules 10-30 correspondingly include sub-modules, units or sub-units for performing the subdivision steps of the foregoing multi-dimensional identity information identification method , I won’t repeat it here.

3, this application also proposes a computer device, including a memory 1003 and a processor 1002, the memory 1003 stores a computer program 1004, and the processor 1002 executes the computer program 1004 when implementing any of the above methods, including: According to the target detection algorithm, the identification information of the object to be identified in the video is identified, and the identification probability of the object to be identified in all categories of the preset multi-dimensional identity information to be identified is obtained; where the preset multi-dimensional identity information to be identified includes N categories of first identity information to be identified in the human body feature dimension and M categories of second identity information to be identified in the clothing dimension; preset multi-dimensional identity information to be identified as the identity information corresponding to the target object, and both M and N A positive integer; according to the recognition probability, look up the corresponding first judgment result in the first preset judgment table to determine whether the corresponding type of preset multi-dimensional identity information to be identified is recognized according to the first judgment result; in the first preset judgment In the table, if the types of the preset multi-dimensional identity information to be identified are different, the judgment conditions corresponding to the recognition probability are not completely the same; according to the preset statistical rules, the multiple first judgment results are counted, and the second pre- It is assumed that the corresponding second determination result is searched in the determination table to determine whether the object to be identified matches the target object; second determination results corresponding to different statistical results are preset in the second preset determination table.

4, this application also proposes a computer storage medium 2001. The storage medium 2001 may be a non-volatile storage medium or a volatile storage medium. A computer program 2002 is stored thereon. When the computer program 2002 is executed by the processor, the steps of any one of the above methods include: identifying the object to be identified in the video according to the target detection algorithm to obtain the object to be identified Recognition probabilities corresponding to all categories of preset multi-dimensional identity information to be recognized; wherein, the preset multi-dimensional identity information to be recognized includes N categories of first identity information to be recognized in the human body feature dimension and M categories in the clothing dimension The second identity information to be identified; the preset multi-dimensional identity information to be identified is the identity information corresponding to the target object, M and N are both positive integers; according to the identification probability, look up the corresponding first judgment result in the first preset judgment table , To determine whether the preset multi-dimensional identity information of the corresponding category is recognized according to the first determination result; in the first preset determination table, if the categories of the preset multi-dimensional identity information to be recognized are different, the identification probability corresponds to the determination The conditions are not exactly the same; the multiple first judgment results are counted according to the preset statistical rules, and the corresponding second judgment results are searched in the second preset judgment table according to the statistical results to determine whether the object to be identified is the target object Match; the second judgment results corresponding to different statistical results are preset in the second preset judgment table.

The above are only preferred embodiments of this application, and do not limit the scope of this application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of this application, or directly or indirectly used in other related technical fields , The same reason is included in the scope of patent protection of this application.

Claims

A multi-dimensional identification information identification method, which includes:

The identification information of the object to be identified in the video is identified according to the target detection algorithm, and the identification probability of the object to be identified in all categories of the preset multi-dimensional identity information to be identified is obtained; wherein, the preset number of identification information to be identified The dimensional identity information includes N categories of first identity information to be identified in the human body feature dimension and M categories of second identity information to be identified in the clothing dimension; the preset multi-dimensional identity information to be identified is the identity information corresponding to the target object , The M and N are both positive integers;

Search for the corresponding first determination result in the first preset determination table according to the recognition probability to determine whether the corresponding category of the preset multi-dimensional identity information to be recognized is recognized according to the first determination result; In the first preset determination table, if the types of the preset multi-dimensional identity information to be recognized are different, the determination conditions corresponding to the recognition probability are not completely the same;

Perform statistics on a plurality of the first determination results according to preset statistical rules, and look up the corresponding second determination results in the second preset determination table according to the statistical results to determine whether the object to be identified is the same as the target object Match; the second judgment results corresponding to different statistical results are preset in the second preset judgment table.
The multi-dimensional identity information identification method according to claim 1, wherein the identity information of the object to be identified in the video is identified according to the target detection algorithm, and it is obtained that the object to be identified is preset in the multi-dimensional identity information to be identified The steps corresponding to the recognition probability of all categories include:

Separating the object to be recognized from the background of the video to be recognized by an image segmentation algorithm;

Performing part key point detection on the separated object to be recognized, and segmenting the recognition area of the object to be recognized according to the part key point, the recognition area including a head, an upper body, and a lower body;

Identify the to-be-identified identity information of respective corresponding categories in the identification area, and obtain the corresponding identification probability.
The method for identifying multi-dimensional identity information according to claim 1, wherein the corresponding first determination result is searched in a first preset determination table according to the recognition probability to determine whether to recognize or not according to the first determination result The step of getting the preset multi-dimensional identity information to be identified in the corresponding category includes:

Searching for a corresponding determination condition in the first preset determination table according to the corresponding category of the recognition probability; the determination condition includes the preset probability threshold of the recognition probability in the corresponding category;

Comparing the recognition probability with the preset probability threshold; the preset probability threshold includes a first preset probability threshold and a second preset probability threshold;

If the recognition probability is higher than the first preset probability threshold, determining that the corresponding category of the identity information to be recognized is recognized according to the first determination result;

If the recognition probability is higher than the second preset probability threshold and lower than the first preset probability threshold, it is determined according to the first determination result whether it is uncertain whether the identity information to be recognized of the corresponding category is recognized;

If the recognition probability is lower than the second preset probability threshold, it is determined according to the first determination result that the identity information to be recognized of the corresponding category is not recognized.
The multi-dimensional identity information identification method according to claim 1, wherein the step of performing statistics on a plurality of the first determination results according to a preset statistical rule comprises:

According to the preset statistical rule, if the identification information to be identified is determined according to the first determination result, the statistical score is assigned to A; if it is determined according to the first determination result that it is uncertain whether it is identified For the identity information to be identified, the statistical score is assigned a value of B; if it is determined that the identity information to be identified is not identified according to the first determination result, the statistical score is assigned a score of C;

The statistical scores of all the first determination results are superimposed and calculated to obtain the statistical results.
The multi-dimensional identity information identification method according to claim 4, wherein the step of superimposing all the statistical scores of the first determination result to obtain the statistical result comprises:

Acquiring the statistical weights corresponding to the preset to-be-identified multi-dimensional identity information of different categories in the preset weight allocation table;

According to the first formula, the statistical scores of all the first determination results are superimposed and calculated to obtain the statistical results; wherein, the first formula is:

P=W1*A+W2*B+W3*C;

The W1, W2, and W3 are the statistical weights corresponding to the preset multi-dimensional identity information to be identified in different categories.
The method for identifying multi-dimensional identity information according to claim 5, wherein the step of obtaining the corresponding statistical weights of the preset to-be-identified multi-dimensional identity information of different categories in a preset weight distribution table comprises:

Obtain pre-associated current environmental factors according to the preset categories of the multi-dimensional identity information to be identified. The current environmental factors include one or more of current temperature, current air quality, current visibility, current geographic location, and height from the ground. A combination of environmental factors; wherein, the current environmental factors pre-associated with the preset multi-dimensional identity information to be identified in different categories are not completely the same;

The corresponding statistical weight is determined in the preset weight distribution table according to the preset type of the multi-dimensional identity information to be identified and the current environmental factor; the preset weight distribution table corresponds to the preset The categories of the multi-dimensional identity information to be identified are set with multiple threshold ranges of environmental factors, and different threshold ranges of environmental factors correspond to different statistical weights.
The multi-dimensional identity information identification method according to claim 1, wherein the identification information of the object to be identified in the video is identified according to the target detection algorithm, and the identification information of the object to be identified is obtained in the preset number of identification information. Before the step of identifying the corresponding probabilities of all categories of dimensional identity information, it also includes:

The preset multi-dimensional identity information to be identified is obtained by receiving the input setting instruction of the preset multi-dimensional identity information to be identified or performing identity information identification on the designated target object through a target detection algorithm.
A multi-dimensional identity information recognition device, which includes:

The recognition module is used to recognize the identity information of the object to be recognized in the video according to the target detection algorithm, and obtain the corresponding recognition probability of the object to be recognized in all categories of preset multi-dimensional identity information to be recognized; wherein, the The preset multi-dimensional identity information to be identified includes N categories of first identity information to be identified in the human body feature dimension and M categories of second identity information to be identified in the clothing dimension; the preset multi-dimensional identity information to be identified is a target Identity information corresponding to the object, where both M and N are positive integers;

The first search module is configured to search for the corresponding first determination result in the first preset determination table according to the recognition probability, so as to determine whether the corresponding category of the preset to-be-recognized multiple is recognized according to the first determination result. Dimensional identity information; the judgment condition of the recognition probability is preset in the first preset judgment table, wherein, if different types of the preset multi-dimensional identity information to be identified have different types, the recognition probability The corresponding judgment conditions are not exactly the same;

The second search module is configured to perform statistics on a plurality of the first determination results according to preset statistical rules, and search for the corresponding second determination results in the second preset determination table according to the statistical results, to determine the to-be-identified Whether the object matches the target object; the second judgment result corresponding to the different statistical result is preset in the second preset judgment table.
A computer device includes a memory and a processor. The memory stores a computer program. The steps of a method for identifying dimensional identity information are implemented when the processor executes the computer program:

The identification information of the object to be identified in the video is identified according to the target detection algorithm, and the identification probability of the object to be identified in all categories of the preset multi-dimensional identity information to be identified is obtained; wherein, the preset number of identification information to be identified The dimensional identity information includes N categories of first identity information to be identified in the human body feature dimension and M categories of second identity information to be identified in the clothing dimension; the preset multi-dimensional identity information to be identified is the identity information corresponding to the target object , The M and N are both positive integers;

Search for the corresponding first determination result in the first preset determination table according to the recognition probability to determine whether the corresponding category of the preset multi-dimensional identity information to be recognized is recognized according to the first determination result; In the first preset determination table, if the types of the preset multi-dimensional identity information to be recognized are different, the determination conditions corresponding to the recognition probability are not completely the same;

Perform statistics on a plurality of the first determination results according to preset statistical rules, and look up the corresponding second determination results in the second preset determination table according to the statistical results to determine whether the object to be identified is the same as the target object Match; the second judgment results corresponding to different statistical results are preset in the second preset judgment table.
The computer device according to claim 9, wherein the identification information of the object to be identified in the video is identified according to the target detection algorithm, and it is obtained that the object to be identified is in all categories of preset multi-dimensional identity information to be identified The corresponding identification probability steps include:

Separating the object to be recognized from the background of the video to be recognized by an image segmentation algorithm;

Performing part key point detection on the separated object to be recognized, and segmenting the recognition area of the object to be recognized according to the part key point, the recognition area including a head, an upper body, and a lower body;

Identify the to-be-identified identity information of respective corresponding categories in the identification area, and obtain the corresponding identification probability.
8. The computer device according to claim 9, wherein the corresponding first determination result is searched in a first preset determination table according to the recognition probability, so as to determine whether the corresponding category is recognized according to the first determination result The step of presetting multi-dimensional identity information to be identified includes:

Searching for a corresponding determination condition in the first preset determination table according to the corresponding category of the recognition probability; the determination condition includes the preset probability threshold of the recognition probability in the corresponding category;

Comparing the recognition probability with the preset probability threshold; the preset probability threshold includes a first preset probability threshold and a second preset probability threshold;

If the recognition probability is higher than the first preset probability threshold, determining that the corresponding category of the identity information to be recognized is recognized according to the first determination result;

If the recognition probability is higher than the second preset probability threshold and lower than the first preset probability threshold, it is determined according to the first determination result whether it is uncertain whether the identity information to be recognized of the corresponding category is recognized;

If the recognition probability is lower than the second preset probability threshold, it is determined according to the first determination result that the identity information to be recognized of the corresponding category is not recognized.
The computer device according to claim 9, wherein the step of performing statistics on a plurality of the first determination results according to a preset statistical rule comprises:

According to the preset statistical rule, if the identification information to be identified is determined according to the first determination result, the statistical score is assigned to A; if it is determined according to the first determination result that it is uncertain whether it is identified For the identity information to be identified, the statistical score is assigned a value of B; if it is determined that the identity information to be identified is not identified according to the first determination result, the statistical score is assigned a score of C;

The statistical scores of all the first determination results are superimposed and calculated to obtain the statistical results.
11. The computer device according to claim 12, wherein the step of superimposing all the statistical scores of the first determination result to obtain the statistical result comprises:

Acquiring the statistical weights corresponding to the preset to-be-identified multi-dimensional identity information of different categories in the preset weight allocation table;

According to the first formula, the statistical scores of all the first determination results are superimposed and calculated to obtain the statistical results; wherein, the first formula is:

P=W1*A+W2*B+W3*C;

The W1, W2, and W3 are the statistical weights corresponding to the preset multi-dimensional identity information to be identified in different categories.
The computer device according to claim 13, wherein the step of obtaining the corresponding statistical weights of the preset to-be-identified multi-dimensional identity information of different categories in a preset weight distribution table comprises:

Obtain pre-associated current environmental factors according to the preset categories of the multi-dimensional identity information to be identified. The current environmental factors include one or more of current temperature, current air quality, current visibility, current geographic location, and height from the ground. A combination of environmental factors; wherein, the current environmental factors pre-associated with the preset multi-dimensional identity information to be identified in different categories are not completely the same;

The corresponding statistical weight is determined in the preset weight distribution table according to the preset type of the multi-dimensional identity information to be identified and the current environmental factor; the preset weight distribution table corresponds to the preset The categories of the multi-dimensional identity information to be identified are set with multiple threshold ranges of environmental factors, and different threshold ranges of environmental factors correspond to different statistical weights.
The computer device according to claim 9, wherein the identification information of the object to be identified in the video is identified according to the target detection algorithm, and the identification information of the object to be identified in the preset multi-dimensional identity information to be identified is obtained. Before the steps of identifying the probabilities corresponding to all categories, it also includes:

The preset multi-dimensional identity information to be identified is obtained by receiving the input setting instruction of the preset multi-dimensional identity information to be identified or performing identity information identification on the designated target object through a target detection algorithm.
A storage medium having a computer program stored thereon, wherein the steps of a method for identifying dimensional identity information when the computer program is executed by a processor are as follows:

The identification information of the object to be identified in the video is identified according to the target detection algorithm, and the identification probability of the object to be identified in all categories of the preset multi-dimensional identity information to be identified is obtained; wherein, the preset number of identification information to be identified The dimensional identity information includes N categories of first identity information to be identified in the human body feature dimension and M categories of second identity information to be identified in the clothing dimension; the preset multi-dimensional identity information to be identified is the identity information corresponding to the target object , The M and N are both positive integers;

Search for the corresponding first determination result in the first preset determination table according to the recognition probability to determine whether the corresponding category of the preset multi-dimensional identity information to be recognized is recognized according to the first determination result; In the first preset determination table, if the types of the preset multi-dimensional identity information to be recognized are different, the determination conditions corresponding to the recognition probability are not completely the same;

Perform statistics on a plurality of the first determination results according to preset statistical rules, and look up the corresponding second determination results in the second preset determination table according to the statistical results to determine whether the object to be identified is the same as the target object Match; the second judgment results corresponding to different statistical results are preset in the second preset judgment table.
The storage medium according to claim 16, wherein the identification information of the object to be identified in the video is identified according to the target detection algorithm to obtain that the object to be identified is in all categories of preset multi-dimensional identification information to be identified The corresponding identification probability steps include:

Separating the object to be recognized from the background of the video to be recognized by an image segmentation algorithm;

Performing part key point detection on the separated object to be recognized, and segmenting the recognition area of the object to be recognized according to the part key point, the recognition area including a head, an upper body, and a lower body;

Identify the to-be-identified identity information of respective corresponding categories in the identification area, and obtain the corresponding identification probability.
The storage medium according to claim 16, wherein the corresponding first determination result is searched in a first preset determination table according to the recognition probability, so as to determine whether the corresponding category is recognized according to the first determination result The step of presetting multi-dimensional identity information to be identified includes:

Searching for a corresponding determination condition in the first preset determination table according to the corresponding category of the recognition probability; the determination condition includes the preset probability threshold of the recognition probability in the corresponding category;

Comparing the recognition probability with the preset probability threshold; the preset probability threshold includes a first preset probability threshold and a second preset probability threshold;

If the recognition probability is higher than the first preset probability threshold, determining that the corresponding category of the identity information to be recognized is recognized according to the first determination result;

If the recognition probability is higher than the second preset probability threshold and lower than the first preset probability threshold, it is determined according to the first determination result whether it is uncertain whether the identity information to be recognized of the corresponding category is recognized;

If the recognition probability is lower than the second preset probability threshold, it is determined according to the first determination result that the identity information to be recognized of the corresponding category is not recognized.
The storage medium according to claim 16, wherein the step of performing statistics on a plurality of the first determination results according to a preset statistical rule comprises:

According to the preset statistical rule, if the identification information to be identified is determined according to the first determination result, the statistical score is assigned to A; if it is determined according to the first determination result that it is uncertain whether it is identified For the identity information to be identified, the statistical score is assigned a value of B; if it is determined that the identity information to be identified is not identified according to the first determination result, the statistical score is assigned a score of C;

The statistical scores of all the first determination results are superimposed and calculated to obtain the statistical results.
18. The storage medium according to claim 19, wherein the step of performing a superposition calculation on the statistical scores of all the first determination results to obtain the statistical results comprises:

Acquiring the statistical weights corresponding to the preset to-be-identified multi-dimensional identity information of different categories in the preset weight allocation table;

According to the first formula, the statistical scores of all the first determination results are superimposed and calculated to obtain the statistical results; wherein, the first formula is:

P=W1*A+W2*B+W3*C;

The W1, W2, and W3 are the statistical weights corresponding to the preset multi-dimensional identity information to be identified in different categories.