Dual collaborative representation based discriminant projection for face recognition

Collaborative representation based techniques have shown promising results for face recognition; however, most of them code the samples by taking the overall samples as a dictionary, which may contain much noise information. To tackle this problem, a new face recognition algorithm, namely dual collaborative representation based discriminant projection (DCRDP), is proposed in this paper. In DCRDP, each training sample is reconstructed via dual collaborative representation to enhance the robustness to noise information: the first collaborative representation is used to choose an appropriate dictionary with respect to the training sample, while the second collaborative representation is used to find collaborative representation relationships between samples. After dual collaborative representation, DCRDP constructs two adjacency graphs to model the similarity and dissimilarity between samples, and then finds the optimal projection matrix for dimension reduction. Experiments on ExtYaleB, AR and CMU PIE face datasets verify the superiority of DCRDP to some other state-of-the-art approaches.