Two-dimensional discriminant transform for face recognition

被引：252
|
作者：
Yang, J [1 ]
Zhang, D
Yong, X
Yang, JY
机构：
[1] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China
[2] Nanjing Univ Sci & Technol, Dept Comp Sci, Nanjing 210094, Peoples R China
来源：
PATTERN RECOGNITION | 2005年 / 38卷 / 07期
基金：
中国国家自然科学基金;
关键词：
fisher linear discriminant analysis (FLD or LDA); fisherfaces; feature extraction; face recognition; two-dimensional data analysis;
D O I：
10.1016/j.patcog.2004.11.019
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 develops a new image feature extraction and recognition method coined two-dimensional linear discriminant analysis (2DLDA). 2DLDA provides a sequentially optimal image compression mechanism, making the discriminant information compact into the up-left corner of the image. Also, 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner. 2DLDA is tested and evaluated using the AT&T face database. The experimental results show 2DLDA is more effective and computationally more efficient than the current LDA algorithms for face feature extraction and recognition. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：1125 / 1129
页数：5
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