2D-LDA: A statistical linear discriminant analysis for image matrix

被引：502

作者：

Li, M ^{[1
]}

Yuan, BZ ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Inst Sci Informat, Beijing 100044, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2005年 / 26卷 / 05期

基金：

中国国家自然科学基金;

关键词：

feature extraction; image representation; linear discriminant analysis; subspace techniques; face recognition;

D O I：

10.1016/j.patrec.2004.09.007

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 proposes an innovative algorithm named 2D-LDA, which directly extracts the proper features from image matrices based on Fisher's Linear Discriminant Analysis. We experimentally compare 2D-LDA to other feature extraction methods, such as 2D-PCA, Eigenfaces and Fisherfaces. And 2D-LDA achieves the best performance. (c) 2004 Elsevier B.V. All rights reserved.

引用

页码：527 / 532

页数：6

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