Fast Recognition of Multi-View Faces with Feature Selection

Abstract

We propose a discriminative feature selection method utilizing support vector machines for the challenging task of multiview face recognition. According to the statistical relationship between the two tasks, feature selection and multiclass classification, we integrate the two tasks into a single consistent framework and effectively realize the goal of discriminative feature selection. The classification process can be made faster without degrading the generalization performance through this discriminative feature selection method. On the UMIST multiview face database, our experiments show that this discriminative feature selection method can speed up the multiview face recognition process without degrading the correct rate and outperform the traditional kernel subspace methods.