Low-Rank and Eigenface Based Sparse Representation for Face Recognition

被引:3
作者
Hou, Yi-Fu [1 ]
Sun, Zhan-Li [1 ]
Chong, Yan-Wen [2 ]
Zheng, Chun-Hou [1 ]
机构
[1] Anhui Univ, Coll Elect Engn & Automat, Hefei 230039, Peoples R China
[2] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China
基金
美国国家科学基金会;
关键词
D O I
10.1371/journal.pone.0110318
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];
摘要
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.
引用
收藏
页数:14
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