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.