Single sample face recognition method based on local subspace sparse representation

Abstract

The invention discloses a single sample face recognition method based on local subspace sparse representation. The single sample face recognition method includes the steps that firstly, block and subblock partition is conducted on face images, and it is assumed that subblocks in the same block are located in the same sub space; secondly, center subblocks of the corresponding blocks of tested images are represented through all the subblocks in the corresponding blocks of all training samples based on sparse representation, and expression coefficients are computed; on the basis of this, reconstitution residual errors of all categories are worked out, and the categories of the tested image blocks are determined according to the minimum residual error principle; finally, weighted voting is conducted on all the tested image blocks, a classification result is determined finally, and the weight of all the blocks can be worked out according to the sparse concentration ratio of the sparse representation coefficients. By means of the single sample face recognition method, good robustness is achieved for the face, the illumination variation, blocking and the like, the recognition accuracy is high, efficient concurrent computation is supported, and a simple and effective solution scheme is provided for the single sample face recognition problem.

Description

technical field [0001] The invention belongs to the field of image processing and analysis, and relates to a new and effective single-sample face recognition solution, in particular to a face automatic recognition method in which each object to be recognized has only one training image. Background technique [0002] As one of the biometric identification technologies, face recognition has attracted increasing attention from academia and industry due to its prominent features such as strong intuition, high user acceptance, not easy to counterfeit, and easy to collect, and has been used in public security, financial services, Many fields that require identity authentication, such as human-computer interaction, are playing an increasingly important role. Among the many existing face recognition methods, the face recognition method based on sparse representation proposed by J.Wright et al. in recent years (A. Yang, A. Ganesh, S. Sastry, and Y. Ma, "Robust Face Recognition via S...

AI Technical Summary

Problems solved by technology

However, when there is usually only a single training sample in many real-world applications such as ID card recognition, customs passport verification, and security monitoring, the recognition performance of these methods will drop sharply or even be completely infeasible.

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