Sparse Low-Rank Component Coding for Face Recognition with Illumination and Corruption

Sparse representation-based classification shows a good performance for face recognition in recent years, but it can not be suitable for face recognition with illumination and corruption, which are often presented in the practical applications. To solve the problem, in this paper, we propose a novel SRC based method for face recognition named sparse low-rank component coding (SLC). In SLC, we utilize the low-rank component from training dataset to construct dictionary. The dictionary composed of low-rank component is able to describe the face feature better, especially for training samples with illumination and corruption. Our recognition rule is based on the minimum class-wise reconstruction residual which leads to a substantial improvement on the performance of SLC. Extensive experiments on benchmark face databases demonstrate that the proposed method consistently outperforms the other sparse representation based approaches for face recognition with illumination and corruption.