Enhanced two-dimension scatter difference discriminant analysis for face recognition

被引：0

作者：

Chen, Cai-kou ^{[1
]}

Yang, Jing-yu ^{[2
]}

机构：

[1] Yangzhou Univ, Informat Engn Coll, Yangzhou, Peoples R China

[2] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing, Peoples R China

来源：

FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 4, PROCEEDINGS | 2007年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/FSKD.2007.269

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 model for image feature extraction and recognition called enhanced two-dimension scatter difference discriminant analysis (E2DSDD) is presented in the paper 2DSDD can extract less coefficients than the traditional two-dimension scatter difference discriminant analysis (2DSDD) for image representation and lead to faster classification. In addition, a new feature selection scheme is suggested for the selection of the most discriminative features. Experiments on the ORL face databases show E2DSDD outperforms the current 2DSDD, 2DLDA and 2DPCA algorithms in its computation efficiency and recognition performance.

引用

页码：704 / +

页数：3

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