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