Face Recognition Using A Low Rank Representation Based Projections Method

被引：3
|
作者：
Wang, Zhenyu [1 ,2 ]
Yang, Wankou [1 ,2 ,3 ]
Shen, Fumin [4 ]
机构：
[1] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
[2] Southeast Univ, Minist Educ, Key Lab Measurement & Control Complex Syst Engn, Nanjing 210096, Jiangsu, Peoples R China
[3] Southeast Univ, Minist Educ, Key Lab Child Dev & Learning Sci, Nanjing 210096, Jiangsu, Peoples R China
[4] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2016年 / 43卷 / 03期
关键词：
Sparse representation; Low rank representation; Feature extraction; Face recognition; DIMENSIONALITY REDUCTION; DISCRIMINANT; CLASSIFIER;
D O I：
10.1007/s11063-015-9448-z
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a low rank representation based projections (LRRP) method is presented for face recognition. In LRRP, low rank representation is used to construct a nuclear graph to characterize the local compactness information by designing the local scatter matrix like SPP; the total separability information is characterized by the total scatter like PCA. LRRP seeks the projection matrix simultaneously maximizing the total separability and the local compactness. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that LRRP works well for face recognition.
引用
收藏
页码：823 / 835
页数：13
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