Two-dimensional Heteroscedastic Discriminant Analysis and Applications in Face Recognition
Si-Bin He · 2009
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.