A Novel Face Feature Extraction Method Based on Two-dimensional Principal Component Analysis and Kernel Discriminant Analysis

被引：1

作者：

Wang, Xiaoguo ^{[1
]}

Liu, Jun ^{[1
]}

Tian, Ming ^{[1
]}

Huang, Yong ^{[1
]}

Cao, Tieyong ^{[1
]}

Zhang, Xiongwei ^{[1
]}

机构：

[1] PLA Univ Sci & Tech, Inst Commun Engn, Nanjing 210007, Peoples R China

来源：

2009 INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT AND ENGINEERING, PROCEEDINGS | 2009年

关键词：

feature extraction; face recognition; principal component analysis; Kernel discriminant analysis;

D O I：

10.1109/ICIME.2009.130

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A novel face feature extraction method based on Bilateral Two-dimensional Principal Component Analysis (B2DPCA) and Kernel Discriminant Analysis (KDA) was presented in this paper. In this method, B2DPCA method directly extracts the proper features from image matrices at first, then the KDA was performed on the features to enhance discriminant power. As opposed to PICA, B2DPCA is based on 2D image matrices rather than 1D vector so the image matrix does not need to be transformed into a vector prior to feature extraction. Experiments on ORL and Yale face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm.

引用

页码：196 / 200

页数：5

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