Face Recognition Based on Multi-task Joint Discrimination Sparse Representation

doi:10.3969/j.issn.1006-2475.2019.10.014

Abstract

Abstract: In order to solve the problem that the recognition rate is not high due to the influence of attitude, illumination and noise in face recognition, a face recognition method based on multi-task joint discrimination sparse representation is proposed. Firstly, the local binary features of human face are extracted, and an over-complete dictionary learning objective function of joint classification error and representation error is established based on multiple features. Then, using a multi-task joint discriminant dictionary learning method, the multi-task joint discriminant dictionary and the corresponding classifier are learned. The dictionary has good characterization and discriminant ability, so as to improve the face recognition effect. Experimental results show that the proposed method has better recognition performance than other sparse face recognition methods.

Key words: face recognition, joint sparse representation, local binary pattern, dictionary learning

CLC Number:

TP391

LI Lei, REN Yue-mei. Face Recognition Based on Multi-task Joint Discrimination Sparse Representation[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.10.014.

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