Graph embedding low-rank sparse representation recovery sparse representation face recognition method

Abstract

The invention discloses a graph embedding low-rank sparse representation recovery sparse representation face recognition method and belongs to the technical field of computer vision and mode recognition. The method comprises the steps that firstly, a graph embedding low-rank sparse representation recovery method is provided, a clean training sample data matrix with the high discrimination power can be recovered from a training sample data matrix, and meanwhile a training sample data error matrix is obtained; secondly, the sparse representation coefficient of face data to be recognized is obtained by using the clean training sample data matrix as a dictionary, using the training sample data error matrix as an error dictionary, and adopting the norm optimization technology; thirdly, according to the sparse representation coefficient of the face data to be recognized, class association reconstruction is carried out on the face data to be recognized; finally, a face image to be recognized is recognized based on the class association reconstruction error of the face data to be recognized. According to the graph embedding low-rank sparse representation recovery sparse representation face recognition method, the problem that the face recognition effect is poor on the conditions that a training sample image and an image to be recognized are polluted by noise or partially blocked can be solved.

Description

technical field [0001] The invention relates to the technical field of computer vision and pattern recognition, in particular to a face recognition method of graph embedding low-rank sparse representation and restoring sparse representation. Background technique [0002] At present, face recognition technology has become one of the hot research topics in the field of computer vision and pattern recognition technology due to its wide application in identity recognition, video retrieval, security monitoring, etc. [0003] In recent years, with the compressive sensing theory and l 1 The development of norm optimization technology, sparse representation has attracted the attention of many scholars at home and abroad. Under sparse representation, a signal can be represented as the sparsest linear combination of the given dictionary atoms. Studies have shown that sparse representation models are very similar in principle to the human visual system. Therefore, sparse representat...

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Problems solved by technology

[0006] The purpose of the present invention is to provide a face recognition method for graph embedding low-rank sparse representation and restoring sparse representation, the purpose is to solve the problem of face recognition when both the training sample image and the image to be recognized are polluted by noise or partially occluded

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