Single-sample face recognition method based on block linear reconstruction discriminant analysis

Abstract

The invention discloses a single-sample face recognition method based on block linear reconstruction discriminant analysis. The method comprises the steps of firstly, partitioning each face training image, then expressing any face image block as a linear combination of k1 intra-class nearest neighbor image blocks, and meanwhile expressing any face image block as a linear combination of k2 inter-class nearest neighbor image blocks; respectively solving the intra-class representation coefficient and the inter-class representation coefficient by using a least square method, and calculating the intra-class reconstruction divergence and the inter-class reconstruction divergence of the sample; solving an optimal projection matrix by maximizing the ratio of the inter-class reconstruction divergence to the intra-class reconstruction divergence, and extracting features of the training sample set and the to-be-identified sample by using the projection matrix; and finally, constructing a discrimination criterion of the class label of the to-be-recognized face image, and judging the class label of the to-be-recognized face image. According to the method, the problem of single-sample face recognition can be effectively solved, the influence of changes of image illumination, face postures, expressions and the like on the recognition effect can be effectively avoided, and the recognition rateis increased.

Description

technical field [0001] The invention relates to a face image recognition method, in particular to a single-sample face recognition method based on block linear reconstruction 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. In the past few decades, researchers have proposed many face recognition methods, among which the method based on feature extraction is widely used in face recognition. [0003] The more famous feature extraction methods are: [0004] (1) Principal component analysis (PCA), which aims to find a set of projection directions so that after the sample is projected, the overall divergence of the...

AI Technical Summary

Problems solved by technology

[0015] The technical problem to be solved by the present invention is that the feature extraction method based on the block technology in the background technology cannot describe the neighborhood relationship between the samples, and the reconstruction relationship between the samples cannot be desc

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