Block sparse structure low-rank representation based single-sample human face identification method

Abstract

The invention discloses a block sparse structure low-rank representation based single-sample human face identification method. The method comprises the following steps: dividing a human face into a plurality of blocks, diving each block into a plurality of overlapped sub-blocks and supposing that the sub-blocks in the same block is in the same sub-space; based on a low-rank representation model, performing low-rank representation on a test matrix formed by the center sub-blocks of the corresponding blocks of all the test image by a local dictionary formed by all the sub-blocks in corresponding blocks of all training samples to realize effective division of the sub-spaces corresponding to each person, adding block sparse constraint to enhance the identification property of the model, and solving the model by a non-strict augmented lagrangian multiplication to obtain a low-rank representation coefficient matrix; on this basis, classifying the test image blocks by judging the value of the representation coefficient; finally, performing voting on all the test image blocks to finally determine the classification result. The block sparse structure low-rank representation based single-sample human face identification method has high robustness on expression, illumination variation, shielding and the like, has high identification accuracy and supports efficient parallel computation.

Description

technical field [0001] The invention relates to a single-sample face recognition method, in particular to a single-sample face recognition method based on block sparse structure low-rank representation in which each object to be recognized has only one training image, and belongs to the technical field of face recognition. Background technique [0002] Automatic face recognition is a technology that uses computers to analyze face images or videos to identify identities. As the most direct, natural and easily accepted biometric technology, automatic face recognition has always been one of the hottest research topics in the field of pattern recognition and computer vision, and it has great theoretical and practical applications. important research significance. In theory, the in-depth study and effective solution of automatic face recognition can greatly promote the development of pattern recognition, image processing and analysis, computer vision, neural computing and cognit...

AI Technical Summary

Problems solved by technology

In this case, the performance of many traditional face recognition methods and face recognition methods based on sparse or collaborative representations degrades severely or even fails to work.

This is mainly because these traditional methods are not robust enough to sparse noise such as outliers or occlusions and lighting generated in the case of a single sample.

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