Weighting multi-block two-dimensional linear discriminant features for face verification

In order to deal with the small sample size (SSS) problem in linear discriminant analysis (LDA), a two-dimensional linear discriminant analysis (2DLDA) has been proposed [16, 25]. However, some discriminant information could be lost because the 2DLDA can be viewed as operating on the image rows or columns independently [24]. We know that the spatial correlation between neighbouring pixels in both vertical and horizontal directions tend to be high. This motivates us to consider other decompositions of the image. In this paper, a multi-block 2DLDA (MB2DLDA) is developed to consider the spatial correlation in which an image is decomposed into blocks of pixels where some of this local correlations could be captured when the 2DLDA is applied to the subimages instead of the whole image. Moreover, different facial regions have different degrees of importance in face recognition, so we combine the 2DLDA features of the different facial regions based on their degrees of importance. This can be done by weighting the 2DLDA feature of a block. In doing so, the verification performance is improved. We evaluate the approach on the face verification on the XM2VTS database based on a standard protocol defined in the database. The results show that the weighted MB2DLDA outperforms the 2DLDA.