Human face gender recognition method based on multi-parameter index weighting

Abstract

The invention provides a human face gender recognition method based on multi-parameter index weighting. According to the method, through tracking the human face of the same person in a video sequence, a plurality of human face images of the same person are collected; the human face image quality analysis is carried out on the collected human face images; the human face image quality analysis results are used as human face gender recognition accuracy weights for calculating human face gender confidence values subjected to image analysis correction; in addition, the human face gender confidence values of the human face images of the same person are subjected to index weighting operation; and the human face gender is finally judged out according to the calculation results. The human face gender recognition method has the advantages that the gender of a certain person is judged by using a plurality of human faces in the video sequence, so the problem that when a single static picture is taken for human face gender recognition, the interference by in-site environment is greater is solved; and in addition, the problem of recognition accuracy interference due to in-site complicated environment is solved through human face image quality analysis and index weight operation.

Description

technical field [0001] The invention relates to a face gender recognition method based on multi-parameter exponential weighting. Background technique [0002] At present, the methods of face gender recognition at home and abroad are mainly divided into two categories: feature-based methods and statistical-based methods. [0003] The method based on feature analysis refers to the use of low-level features of face images such as hair length, eyebrow thickness, chin width, and beard or not by observing images of men and women as the basis for gender discrimination. The main purpose of this type of method is to realize the gender recognition of face images by measuring the distance of visible features. [0004] Statistical methods treat gender identification as a binary classification problem. Through the study of a large number of training samples, a classifier that can correctly identify the gender of the face on the image is established, and then the classifier classifies t...

AI Technical Summary

Problems solved by technology

In the video surveillance scene, the on-site environment is more complex, and the angle and illumination of the face captured by the camera cannot be guaranteed. Therefore, the captured face may have a large angle deflection, incorrect focus, and uneven illumination, which seriously affect the age. gender effect

Therefore, many current face gender recognition algorithms cannot be applied to traditional video surveillance scenarios

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