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论文类型：期刊论文

发表时间：2011-11-01

发表刊物：Advances in Information Sciences and Service Sciences

收录刊物：Scopus、EI

卷号：3

期号：10

页面范围：345-354

ISSN号：19763700

摘要：Very recently, the sparse representation theory in pattern recognition has aroused widespread concern. Herein, we investigated the sparse representation-based face recognition methods. In order to make the representation coefficient vector sparser, a Gabor feature-based sparse representation classification (GSRC) method was presented, which used the holistic Gabor features to construct dictionary to enhance the robustness for the variations of illumination, expression, occlusion and pose. For the GSRC method, the local information provided by the spatial locations of Gabor features is not completely exploited. To rectify this, local Gabor features are first extracted from some spatially partitioned non-overlapping local patches of face images, and then used to construct sparse representation classifiers (component classifiers). Finally, all these component classifiers are combined to form an ensemble classifier by fuzzy fusion method for final classification. Experimental results on the ORL, AR and FERET face databases verified the feasibility and effectiveness of the proposed methods.