Robust face recognition method based on secondary cooperative representation identification projection

Abstract

The invention discloses a robust face recognition method based on secondary cooperative representation identification projection. The method comprises the steps of: screening out K types of samples closely related to training samples through first-time cooperative representation; representing a linear reconstruction training sample through secondary cooperation to obtain a reconstruction coefficient; constructing the intra-class graph and the inter-class graph of the samples through the reconstruction coefficient to describe cohesiveness and separability of the samples; then obtaining a projection matrix by maximizing the inter-class divergence and minimizing the intra-class divergence at the same time, finally extracting the features of a to-be-identified sample and all the training samples by utilizing the obtained projection matrix, and judging the class label of the to-be-identified sample according to a classification criterion. According to the method, the training samples are reconstructed through cooperative representation, the problem of recognition errors caused by illumination, shielding, human face postures and expression changes can be effectively solved, the trainingsamples can be expressed more effectively and accurately, and the high-precision requirement for human face recognition in practical application can be met.

Description

technical field [0001] The invention relates to a face recognition method, which belongs to the technical field of image recognition. Background technique [0002] Face recognition is an important method of identity identification, and has broad application prospects in file management systems, security verification systems, credit card verification, criminal identification in public security systems, monitoring of banks and customs, and human-computer interaction. The face recognition method based on feature extraction is a mainstream face recognition method, which uses dimensionality reduction technology to extract important features in face images, obtain effective identification information in images, and reduce redundant information and noise in images. The influence of the recognition rate, thereby improving the recognition accuracy and recognition speed. [0003] The existing face feature extraction and recognition methods are: [0004] (1) Eigenfaces, which is a fa...

AI Technical Summary

Problems solved by technology

LPP uses the neighbor graph to describe the local structure of the sample, but LPP is an unsupervised method that does not consider the identification structure of the sample, and the neighbor parameters cannot be automatically determined

SPP aims to maintain the sparsity of samples and is robust to noise, but it is an unsupervised algorithm, and it takes a lot of time to solve the sparse coefficients

CRLDP is a supervised method, which uses all samples to cooperatively represent training samples, and compared with SPP, the reconstruction coefficient is solved quickly, but some samples that are irrelevant to training samples or have more redundant information also participate in the reconstruction of training samples said, so the CRLDP recognition rate may be affected

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