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Face recognition method for an advertisement screen based on autonomous learning

A face recognition technology on the screen, applied in character and pattern recognition, data processing applications, marketing, etc., can solve the problems of not actually watching advertisements, difficult for advertisers to put into use on a large scale, and affecting the effect of advertisements.

Active Publication Date: 2019-03-19
CHENGDU REMARK TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] 1. People who face the screen with their backs or sides, or are too far away from the screen, or just walk in front of the screen, they have not actually watched the advertisement, and if they are included in the viewing population, the statistical data will be inaccurate;
[0004] 2. Different people view advertisements from different angles. If the face recognition algorithm can only recognize a person’s frontal face features, but cannot recognize his side face and tilted head features, it will cause the face recognition algorithm to recognize the same person’s face. Misjudging different people from different angles will also cause inaccurate statistics
[0007] At present, there are already some patents related to electronic advertising screens that count the effects of advertising based on face recognition of the viewing audience, but most of them describe concepts, and few of them are implemented into products. The important reason is that electronic advertising screens are used in real environments. The accuracy of face recognition is not high, which affects the evaluation of advertising effectiveness and makes it difficult for advertisers to use it on a large scale

Method used

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  • Face recognition method for an advertisement screen based on autonomous learning
  • Face recognition method for an advertisement screen based on autonomous learning
  • Face recognition method for an advertisement screen based on autonomous learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] A face recognition method for advertising screens based on autonomous learning, mainly comprising the following steps:

[0052] Step S1: Initial classification and merging:

[0053] Track the continuous frames taken. If the face center distance between two consecutive frames is less than a given threshold, it is determined that the two faces belong to the same person; classify and mark the face image frames and extract feature values; The tracked face features are merged under the same face ID;

[0054] Step S2: Secondary merge:

[0055] Learn the sample eigenvalues ​​accumulated in the cycle time, perform secondary screening on the eigenvalues ​​belonging to the same face angle interval, and merge the eigenvalues ​​of the same face ID; use the face recognition algorithm to judge the similarity of face features Degree, if the similarity is greater than the set value, the recognized face features will be merged under the same face ID.

[0056] The continuous frames ca...

Embodiment 2

[0059] This embodiment is optimized on the basis of embodiment 1, such as Figure 5 As shown, in the step S1, if there is a human face that meets the standard in the picture, the face feature value is obtained, and if the center distance between the acquired picture and the human face in the previous frame is less than a given threshold, the human face in the previous frame is used. Face ID, otherwise generate a new face ID; then obtain the angle classification features of the face in the picture. If there are multiple people in the picture acquired in step S1, the picture is divided into multiple areas according to the number of faces, and a single person is processed in each area.

[0060] Such as Image 6 As shown, in the step S2, the feature list under the same face angle classification is taken out, and the elements in the list are compared in pairs. If the similarity is greater than a given threshold, then it is judged whether any of the two face IDs has a virtual paren...

Embodiment 3

[0064] This embodiment is optimized on the basis of embodiment 1 or 2, as image 3 , Figure 4 As shown, in the step S1, when the angle of the human face is in the front face, left side face, right side face, head buried, and head up, the face image frame is classified and marked and the feature value is extracted; the front face is It means that the horizontal angle interval of the human face is (-10, 10) and the pitch angle interval is (-12, 6); the left face refers to that the horizontal angle interval of the human face is (-30, 10) and the pitch angle interval is It is (-12, 6); the right side face refers to the horizontal angle interval of the human face is (10, 30) and the pitch angle interval is (-12, 6); the buried head refers to the horizontal angle interval of the human face is (-10, 10) and the pitch angle interval is (6, 30); the head-up means that the horizontal angle interval of the face is (-10, 10) and the pitch angle interval is (-30, -12).

[0065] The coun...

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Abstract

The invention discloses a face recognition method for an advertisement screen based on autonomous learning, and the method comprises the steps: carrying out the secondary combination: carrying out thelearning of sample feature values accumulated in the period time, carrying out the secondary screening of the feature values in a same face angle interval, and carrying out the combination of the feature values of the same face ID; and judging the similarity of the human face features by adopting a human face recognition algorithm, and if the similarity is greater than a set value, merging the recognized human face features below the same human face ID. Accurate data statistics is realized through an autonomous learning method, so that more accurate audience analysis is provided for advertisement putting on a display screen. According to the method, the recognition rate is improved through autonomous learning, the recognition recall rate can be continuously improved through learning of the sample characteristic values, more accurate face recognition can improve the understanding of an advertiser on audiences, personalized advertisement putting is more accurate, and meanwhile more reliable data support is provided for calculating the putting effect of advertisements.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a face recognition method for advertising screens based on autonomous learning. Background technique [0002] The display screen is used to display advertisements, while the camera is used to capture portraits of people standing in front of the advertising screen. However, there are several problems in randomly taking photos for face recognition during the advertisement display process: [0003] 1. People who face the screen with their backs or sides, or are too far away from the screen, or just walk in front of the screen, they have not actually watched the advertisement, and if they are included in the viewing population, the statistical data will be inaccurate; [0004] 2. Different people view advertisements from different angles. If the face recognition algorithm can only recognize a person’s frontal face features, but cannot recognize his side face and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06Q30/02
CPCG06Q30/0269G06V40/172G06V40/168G06F18/22
Inventor 毛帆曾敏高永俊
Owner CHENGDU REMARK TECH CO LTD
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