Two dimensional (2D）feature extraction using methods such as 2DPCA(two dimensional principal component analysis）and 2DLDA(two dimensional linear discriminant analysis）is of interest in face recognition because it extracts discriminative features faster than one dimensional (1D）discrimination analysis.Recently

diagonal principal component analysis (DiaPCA）is proposed for face recognition based on 2DPCA.DiaPCA reserves the correlations between variations of rows and those of columns of images.It overcomes that the projective vectors of 2DPCA only reflect variations between rows of images and variations between columns of images are omitted

while the omitted variations between columns of images are usually also useful for recognition.However

DiaPCA in particular cannot make full use of discriminative information during process of feature extraction and the projective vectors of 2DLDA also only reflect variations between rows of images

Therefore recognition performance of DiaPCA and 2DLDA is affected.To solve the problem

diagonal linear dicriminant analysis (DiaLDA）was proposed in this paper.Experimental results on ORL and FERET face database demonstrate the proposed algorithm is superior to 2DLDA and DiaPCA method and some existing well known methods.