We propose a discriminative feature selection method utilizing support vector machines for the challenging task of multi-view face recognition. According to the statistical relationship between the two tasks, feature selection and multi-class 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 multi-view face database, our experiments show that this discriminative feature selection method can speed up the multi-view face recognition process without degrading the correct rate and outperform the traditional kernel subspace methods.