Fisher discriminant analysis based on kernel cuboid for face recognition

被引：7
|
作者：
Liu, Xiao-Zhang [1 ]
Zhang, Chen-Guang [1 ]
机构：
[1] Hainan Univ, Coll Informat Sci & Technol, Haikou 570228, Peoples R China
来源：
SOFT COMPUTING | 2016年 / 20卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Kernel cuboid; Kernel discriminant analysis; Face recognition; COMPONENT ANALYSIS; LDA; REPRESENTATION; EIGENFACES; IMAGES;
D O I：
10.1007/s00500-015-1794-2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper builds the concept of kernel cuboid, and proposes a new kernel-based image feature extraction method for face recognition. The proposed method deals with a face image in a block-wise manner, and independently performs kernel discriminant analysis in every block set, using kernel cuboid instead of kernel matrix. Experimental results on the ORL and UMIST face databases show the effectiveness and scalability of the proposed method.
引用
收藏
页码：831 / 840
页数：10
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