Multi-View Face Recognition with Min-Max Modular Support Vector Machines

Abstract

We have applied the min-max modular support vector machine and the part-versus-part task decomposition method to dealing with multi-view face recognition problems. We have demonstrated that face pose information can be easily incorporated into the procedure of dividing a multi-view face recognition problem into a series of relatively easier two-class subproblems. We have performed some experiments on the UMIST database and compared with the standard support vector machines. The experimental results indicate that the minmax modular support vector machine can improve the accuracy of multi-view face recognition and reduce the training time. As a future work, we will perform experiments on large-scale face databases with various face poses. We believe that the min-max modular support vector machine with incorporating pose information into task decomposition will have more advantages over traditional support vector machines in both training time and recognition accuracy when a more number of training samples are available.