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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.

In this paper, we integrate Robust Principal Component Analysis (Robust PCA) and eigenface extraction into the sparse representation based classification. Firstly, the lowrank images are extracted by applying Robust PCA to make the training images as pure as possible. Then, Singular Value Decomposition (SVD) is adopted to extract the eigenfaces from the low-rank images. Finally, we combine these eigenfaces to construct a compact but discriminative dictionary for sparse representation. We evaluate our algorithm on several popular databases, experimental results demonstrate the effectiveness and robustness of our algorithm.