Robust face recognition based on iterative sparse coding and pixel selection
Face recognition based on sparse representation has attracted broad interest in recent years. In many existing sparse coding works, the distribution of error term (coding residual) is modeled with a Laplacian or Gaussian function, which leads to an l1-norm or l2-norm minimization problem. However, it is hard to fit the error term satisfactorily in practice, especially when occlusion or corruption exists. In order to improve the robustness of sparse coding algorithms to outliers, we propose an efficient pixel-selection strategy in this paper, which can pick out unspoiled pixels from a seriously damaged face image. However, it is very challenging to determine the positions of the outliers directly. We propose an iterative coding approach to improve the selection. Extensive experiments demonstrate the robustness and effectiveness of the proposed method in the presence of occlusion and corruption.