Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion

摘要

In this paper, we consider the problem of recognizing human faces from frontal views with varying illumination, as well as occlusion and disguise. Motivated by the latest research on the recovery of low-rank matrix using robust principal component analysis (RPCA), we present a novel approach of robust face recognition by exploiting the sparse error component obtained by RPCA. Compared with low-rank component, it is revealed that the associated sparse error component exhibits more discriminating information which is of benefit to face identification. We define two descriptors (i.e., sparsity and smoothness) to represent characteristic of the sparse error component, and give two recognition protocols (i.e., the weighted based method and the ratio based method) to classify face images. The efficacy of the proposed approach is verified on publicly available databases (i.e., Extended Yale B and AR) with promising results. Meanwhile, the proposed algorithm manifests robustness since it does not assume any explicit prior knowledge about the illumination conditions, as well as the nature of corrupted and occluded regions. Furthermore, the proposed method is not limited to face recognition, also can be extended to other image-based object recognition.