Two-Dimensional Inverse FDA for Face Recognition

被引：0

作者：

Yang, Wankou ^{[1
]}

Yan, Hui ^{[1
]}

Yin, Jun ^{[1
]}

Yang, Jingyu ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Peoples R China

来源：

PROCEEDINGS OF THE 2008 CHINESE CONFERENCE ON PATTERN RECOGNITION (CCPR 2008) | 2008年

关键词：

Inverse FDA; 2D; feature extraction; face recognition;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose a two-dimensional Inverse Fisher Discriminant Analysis (2DIFDA) method for feature extraction and face recognition. This method combines the ideas of two-dimensional principal component analysis and Inverse FDA and it can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the inverse fisher discriminant criterion. Experiments on the FERET face databases show that the new method outperforms the PCA, 2DPCA, Fisherfaces and the inverse fisher discriminant analysis.

引用

页码：227 / 231

页数：5

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