Discriminant Common Vectors in Principal Component Analysis Transformed Space for Face Recognition

In this paper,we first offer an efficient algorithm to perform DCV in principal component analysis(PCA) transformed space.In this way,we reduce the algorithm's complexity and improve the efficiency whilst preserving the whole discriminant information.Then,the new algorithm further facilitates us to subtly weight the facial components in PCA space according to corresponding eigenvalues,which is potential to enrich the representative information and thus improves DCV's recognition performance.The experiments conducted on ORL,YALE and AR face database demonstrate the effectiveness of our method.