Human face identity recognition method based on GB(2D)2PCANet depth convolution model

Abstract

The invention discloses a human face identity recognition method based on a GB(2D)2PCANet depth convolution model.A model training method includes the following steps that preprocessed human face samples are sequentially fed into a first feature extraction layer, multiple sub-blocks are scanned from obtained Gabor feature images, mean removal is conducted, an optimal projection axis is extracted through (2D)2PCA and convoluted with a training set original sample, and a first layer of feature map is obtained; the first layer of feature map is fed into a second feature extraction layer, the steps are repeated, and a second layer of feature map is obtained; a feature map is output in a binarized mode, and local area histograms are calculated and spiced to serve as final features; the final features are fed into a linear SVM classifier, and an optimized human face identity recognition model is obtained.Effective feature expression can be automatically learnt, good locality is achieved, good robustness is achieved for illumination, expressions, noise and the like, and the recognition performance of human face identities is improved.

Description

Technical field: [0001] The invention belongs to the field of machine vision, in particular to a GB(2D) based 2 Face recognition method for PCANet deep convolutional model. Background technique: [0002] Face recognition technology is a technology that uses computers to analyze face videos or images, extract facial features from them, and identify identities through these features. [0003] At present, face recognition technology is developing rapidly, and a large number of research results have been obtained. Common face recognition algorithms can be divided into several categories: face recognition based on geometric features, face recognition based on subspace analysis, face recognition based on elastic matching, and face recognition based on hidden Markov model. Identity recognition, face recognition based on neural network and face recognition based on 3D. For example, Takatsugu et al. [1] used an elastic matching method based on dynamic link structure to locate the ...

AI Technical Summary

Problems solved by technology

[0004] The present invention aims at the problems existing in the above-mentioned face recognition, and proposes a method based on GB(2D) 2 The face recognition method of the PCANet deep convolution model not only absorbs the advantages of the depth model and Gabor filtering, but also can extract more abstract features in the data, and is robust to factors such as illumination, expression, and occlusion, and overcomes convolution. Disadvantages of time-consuming neural network and large number of labels

Images