  In this paper, we propose a novel approach named by Discriminative Principal
Component Analysis which is abbreviated as Discriminative PCA in order to
enhance separability of PCA by Linear Discriminant Analysis (LDA). The proposed
method performs feature extraction by determining a linear projection that
captures the most scattered discriminative information. The most innovation of
Discriminative PCA is performing PCA on discriminative matrix rather than
original sample matrix. For calculating the required discriminative matrix
under low complexity, we exploit LDA on a converted matrix to obtain
within-class matrix and between-class matrix thereof. During the computation
process, we utilise direct linear discriminant analysis (DLDA) to solve the
encountered SSS problem. For evaluating the performances of Discriminative PCA
in face recognition, we analytically compare it with DLAD and PCA on four well
known facial databases, they are PIE, FERET, YALE and ORL respectively. Results
in accuracy and running time obtained by nearest neighbour classifier are
compared when different number of training images per person used. Not only the
superiority and outstanding performance of Discriminative PCA showed in
recognition rate, but also the comparable results of running time.
