A Diagonal Linear Discriminant Analysis Algorithm with Application to Face Recognition
一种对角LDA算法及其在人脸识别上的应用

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.