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ABSTRACT
Due to the variation of the images of a face, limited training samples have high uncertainty for representing a test sample. Moreover, traditional grouped sparse representation method only considers the scores from different groups, not consider the difference between different groups. To address this, in this paper, a novel method is proposed to reduce the uncertainty in face recognition. For a test sample, our method first selects its K nearest training samples to form a sub-training set, where K is smaller than the number of the training samples. Then, each selected training sample and its nearest training sample are simultaneously used to generate two virtual samples. All virtual samples form a virtual sub-training set. Then, an improved grouped spare representation method is derived on the two sets to generate two residuals. Finally, two residuals and a score are fused to classification the test sample. The score is defined as the -norm of the cross product of two residuals. Experimental results on four databases demonstrate that our method is robust and can obtain higher recognition accuracy than the state-of-the-art approaches.