title: 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.
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei, China.
authors.forename: Yi-Fu
authors.initials: YF
authors.lastname: Hou
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei, China.
authors.forename: Zhan-Li
authors.initials: ZL
authors.lastname: Sun
authors.affiliation: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
authors.forename: Yan-Wen
authors.initials: YW
authors.lastname: Chong
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei, China.
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis