A SVM face recognition method based on optimized Gabor features

被引：0
|
作者：
Shen, Linlin [1 ]
Bai, Li [2 ]
Ji, Zhen [1 ]
机构：
[1] Shenzhen Univ, Fac Informat & Engn, Shenzhen 518060, Peoples R China
[2] Univ Nottingham, Sch Comp Sci & Informat Technol, Nottingham NG7 2RD, England
来源：
ADVANCES IN VISUAL INFORMATION SYSTEMS | 2007年 / 4781卷
关键词：
Gabor features; Support Vector Machine; Linear Discriminant Analysis;
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：
A novel Support Vector Machine (SVM) face recognition method using optimized Gabor features is presented in this paper. 200 Gabor features are first selected by a boosting algorithm, which are then combined with SVM to build a two-class based face recognition system. While computation and memory cost of the Gabor feature extraction process has been significantly reduced, our method has achieved the same accuracy as a Gabor feature and Linear Discriminant Analysis (LDA) based multi-class system.
引用
收藏
页码：165 / +
页数：3
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