Locality Preserving Fisher Discriminant Analysis for Face Recognition

被引：0

作者：

Zhao, Xu ^{[1
]}

Tian, Xiaoyan ^{[1
]}

机构：

[1] Beijing Univ Technol, Lab Comp Software & Theory, Beijing, Peoples R China

来源：

EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PROCEEDINGS | 2009年 / 5754卷

关键词：

Fisher Discriminant Criterion; Locality Preserving Projection; Face Recognition;

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：

Dimensionality reduction is a key technology for face recognition. In this paper, we propose a novel method, called Locality Preserving Fisher Discriminant Analysis (LPFDA), which extends the original Fisher Discriminant Analysis by preserving the locality structure of the data. LPFDA can get a sub-space projection matrix by solving a generalized eigenvalue problem. Several experiments are conducted to demonstrate the effectiveness and robustness of our method.

引用

页码：261 / 269

页数：9

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