Network constructing method for human face identification, identification method and system

Abstract

The invention discloses a deeper layer network constructing method used for gender identification or age estimation based on human face. The method includes a step (101) dividing all training pictures into a plurality of groups; (102) extracting high layer features of a group of pictures based on a convolution neural network and thereby obtaining a first matrix composed of the high layer feature vectors, and extracting low layer and global features of the same group of the training images based on an artificial neural network and thereby obtaining a second matrix composed of the low layer feature vectors, obtaining a group of gender identification or age estimation results based on the extract first matrix, the second matrix and the defined judgment formula, wherein the values of a first weight matrix W1, a second weight matrix w2, an offset matrix b and an adjusting weight beta in the defined judgment formula are updated by utilizing an error back propagation algorithm and the final values of the parameters are obtained and the network construction is completed. Judgment of age and gender of a human face is performed based on the judgment formula determined according to the values of the parameters when the network construction is completed.

Description

technical field [0001] The present invention relates to the technical fields of computer vision and deep learning, in particular to a network construction method, recognition method and system for face recognition. Background technique [0002] As an object that is very difficult for computers to recognize and analyze, human face has attracted extensive attention of researchers since the 1990s. Successful and effective face analysis has great application prospects in the fields of intelligent surveillance, video indexing, and demographic information statistics. Face analysis mainly includes face gender recognition and face age estimation. The accuracy of gender classification and the average absolute error of age estimation are the key indicators of the two. [0003] The current related research in the field of face analysis is based on the "manual" determination of feature descriptors to represent faces, combined with classifier algorithms or regression algorithms. Manual...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to overcome the defect that the performance of the traditional method in the existing face analysis technology cannot meet the needs of prac

Images