Robust Face Recognition Based on Singular Value and Sparse Representation
Qing‐Hu Chen · Video Engineering · 2010
At present, there are two main barriers toward positive two-dimensional-based face recognition: one is the variation of illumination and expression, the other is the problem of occlusion and noise. The problem of robust recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and noise will be researched in this paper. The recognition problem is taken as one of classifying among multiple linear regression models, and sparse signal representation is used to solve this problem. The method based on singular value and sparse representation can significantly improve the robustness and reduce the computational complexity. Conducting extensive experiments on publicly available databases verify the efficacy of the proposed algorithm, and corroborate the above claims.