Kernel Non-locality Preserving Projection and Its Application to Face Recognition

被引：0
|
作者：
Wang, Jianguo [1 ]
Yang, Wankou [2 ]
Yan, Hui [3 ]
机构：
[1] Tangshan Coll, Dept Comp Sci & Technol, Tangshan 063000, Peoples R China
[2] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China
[3] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Peoples R China
来源：
PROCEEDINGS OF THE 2009 CHINESE CONFERENCE ON PATTERN RECOGNITION AND THE FIRST CJK JOINT WORKSHOP ON PATTERN RECOGNITION, VOLS 1 AND 2 | 2009年
关键词：
Subspace analysis; Non-locality preserving projection; Face recognition; Kernel mapping; Manifold learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Non-locality preserving projection (NLPP) is a kind of feature extraction technique based on the characterization of the non-local scatter. Due to NLPP is a linear algorithm in nature, it cannot address nonlinear problem in recognition, so a novel subspace method, called Kernel Non-locality Preserving Projection (KNLPP) discriminant analysis, is proposed for face recognition. Experimental results on two popular benchmark databases, FERET and Yale, demonstrate the effectiveness of the proposed method.
引用
收藏
页码：474 / +
页数：2
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