Making FLDA applicable to face recognition with one sample per person

被引：140

作者：

Chen, SC ^{[1
]}

Liu, J

Zhou, ZH

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Dept Comp Sci & Engn, Nanjing 210016, Peoples R China

[2] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210093, Peoples R China

来源：

PATTERN RECOGNITION | 2004年 / 37卷 / 07期

基金：

中国国家自然科学基金;

关键词：

face recognition; Fisher linear discriminant analysis (FLDA); one training sample per person; pattern recognition;

D O I：

10.1016/j.patcog.2003.12.010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In face recognition, the Fisherface approach based on Fisher linear discriminant analysis (FLDA) has obtained some success. However, FLDA fails when each person just has one training face sample available because of nonexistence of the intra-class scatter. In this paper, we propose to partition each face image into a set of sub-images with the same dimensionality, therefore obtaining multiple training samples for each class, and then apply FLDA to the set of newly produced samples. Experimental results on the FERET face database show that the proposed approach is feasible and better in recognition performance than E(PC)(2)A. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1553 / 1555

页数：3