Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry

In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.