Exploiting symmetry in two-dimensional clustering-based discriminant analysis for face recognition
Konstantinos Papachristou, Anastasios Tefas, Ioannis Pitas · 2015
Subspace learning techniques are among the most popular methods for face recognition. In this paper, we propose a novel face recognition technique for two dimensional subspace learning which is able to exploit the symmetry nature of human faces. We extent the Two Dimensional Clustering based Discriminant Analysis (2DCDA) by incorporating an appropriate symmetry regularizer into its objective function in order to determine symmetric projection vectors. The proposed Symmetric Two Dimensional Clustering based Discriminant Analysis technique has been applied to the face recognition problem. Experimental results showed that the proposed technique achieves better classification performance in comparison to the standard one.