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

• Published in: Pattern recognition 2014-02, Vol.47 (2), p.495-508
• Main Authors: Luan, Xiao, Fang, Bin, Liu, Linghui, Yang, Weibin, Qian, Jiye
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Character recognition; Detection, estimation, filtering, equalization, prediction; Errors; Exact sciences and technology; Face recognition; Illumination; Image processing; Information, signal and communications theory; Object recognition; Occlusion; Pattern recognition; Principal component analysis; Robust principal component analysis; Signal and communications theory; Signal processing; Signal, noise; Sparse error; Telecommunications and information theory
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2013.06.031

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. •This paper is motivated by robust principal component analysis (RPCA).•We exploit the sparse error component to perform face recognition.•We define two descriptors (i.e., sparsity and smoothness) to represent the sparse error image.•We present the weighted based method and ratio based method to classify face images.•Our method shows good performance on public face databases with illumination and occlusion.