LAUSR

In sparse representation (SR), a test image is encoded by a sparse linear combination of training samples. The L 1-regulariser used in SR is beneficial to produce a good reconstruction of the test face image with sparse error, but it is incapable to guarantee the robustness against local structural noise. To enhance noise tolerance of SR-based classifier, an improved L 1-regulariser based on trimmed sparse coding (TSC) by using an extra penalty on correlation among all coding coefficients is proposed. Different from traditional single-coding scheme in SR, multiple coding coefficients are used to represent patches of a test image by its corresponding training patches. The consistency penalty imposed into the new SR model improves the confidence for accuracy classification. Experimental results show the superiority of TSC on two benchmark databases, and it outperforms other state-of-the-art methods.