Multi-pose face recognition method based on collaborative fuzzy mean discriminant analysis

Abstract

The invention discloses a multi-pose face recognition method based on collaborative fuzzy mean discriminant analysis, which comprises the steps of acquiring a multi-pose face image training sample set comprising a plurality of different classes, performing normalization on each training sample and a sample to be recognized, and performing dimension reduction by using PCA; calculating the class membership degree of each training sample by using a collaboration representation coefficient of the training samples; calculating a fuzzy class mean; calculating the fuzzy intra-class divergence and the fuzzy inter-class divergence of the training samples; solving a projection matrix through maximizing a ratio of the fuzzy inter-class divergence and the fuzzy intra-class divergence of the training samples, and extracting features of the training samples and the sample to be recognized by using the projection matrix; and judging and determining a class label of the sample to be recognized according to a nearest neighbor classifier. According to the invention, class information of the samples are sufficiently utilized, the similarity of the same class of samples and the difference of different classes of samples are considered, and the robustness for noise and wild points is enhanced through introducing membership degree information when a sample has various changes in illumination, pose and expression.

Description

technical field [0001] The invention relates to a multi-pose face recognition method based on collaborative fuzzy mean value discrimination analysis, 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. Generally speaking, the steps of face recognition can be divided into three parts: one is to detect and segment faces from complex scenes; the other is to extract face features from the found face images; Face features use appropriate algorithms to match and identify faces. Among them, the face image feature extraction is mainly used to reduce the dimensionality of the face image, extract effective identification information in the image, ...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide a multi-pose face recognition method based on collaborative fuzzy mean discriminant analysis, and solve the problem that the existing methods cannot simultaneously consider the similarity of similar samples and the differences of different samples When the sample has many changes in illumination, posture, and expression, it cannot effectively deal with the robustness of noise and wild points. The present invention is based on the membership degree calculation method based on collaborative representation, and uses the obtained membership degree of each sample The fuzzy class mean of the information calculation samples meets the high-precision requirements for multi-pose face recognition in practical applications

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