2维异方差鉴别分析及其在人脸识别中的应用

甘俊英; 何思斌

doi:10.11834/jig.20091034

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2维异方差鉴别分析及其在人脸识别中的应用
Two-dimensional Heteroscedastic Discriminant Analysis and Applications in Face Recognition
2009年14卷第10期页码：2122
纸质出版：2009
DOI： 10.11834/jig.20091034
稿件说明：

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甘俊英, 何思斌. 2维异方差鉴别分析及其在人脸识别中的应用[J]. 中国图象图形学报, 2009,14(10):2122. DOI： 10.11834/jig.20091034.

Two-dimensional Heteroscedastic Discriminant Analysis and Applications in Face Recognition[J]. Journal of Image and Graphics, 2009, 14(10): 2122. DOI： 10.11834/jig.20091034.

摘要

在2维线性鉴别分析(2DLDA)的基础上，介绍了2维异方差鉴别分析(2DHDA)，并将其应用于人脸识别。2DHDA算法去除了2DLDA算法样本类内协方差相等的约束，克服了异方差鉴别分析(HDA)算法的“小样本”问题。首先，根据2DLDA准则定义2DHDA准则；然后，将2DHDA准则取对数，用梯度下降法求得最优投影矩阵，人脸图像向最优投影矩阵投影提取人脸图像的特征；最后，最小距离分类器完成人脸识别。基于ORL与Yale混合人脸数据库的实验结果表明了2DHDA应用于人脸识别的有效性。

Abstract

On the basis of two-dimensional linear discriminant analysis(2DLDA)

a novel discriminant analysis named two-dimensional heteroscedastic discriminant analysis(2DHDA)is introduced

and is used for face recognition. In 2DHDA

equal within-class covariance constraint is removed and “small sample size” problem of heteroscedastic discriminant analysis(HDA)is solved. Firstly

criterion of 2DHDA is defined according to that of 2DLDA. Secondly

criterion of 2DHDA

log term is taken

and then the optimal projection matrix is solved by gradient descent algorithm. Thirdly

facial images are projected onto the optimal projection matrix

then

2DHDA features of face images are extracted. Finally

nearest neighbor classifier is selected to perform face recognition. Experimental results based on olivetti research laboratory(ORL)and Yale mixture face database show the validity of 2DHDA for face recognition.