Low-Rank and Eigenface Based Sparse Representation for Face Recognition

Abstract

In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC). Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA) to alleviate the influence of noises (e.g., illumination difference and occlusions). Secondly, Singular Value Decomposition (SVD) is applied to extract the eigenfaces from these low-rank and approximate images. Finally, we utilize these eigenfaces to construct a compact and discriminative dictionary for sparse representation. We evaluate our method on five popular databases. Experimental results demonstrate the effectiveness and robustness of our method.

Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

“…The investigational results had shown that the categorization accurateness of NNC is greater than that of single feature domain. Improved SRC face recognition system using low-rank representation and eigenface extraction has been presented in [13]. Initially, robust PCA extraction is applied to the low-rank images of the face images of each individual in training subset to lessen the effect of occlusions and illumination difference.…”