Face recognition from sets of images

This paper introduces a novel method for face recognition based on multiple images. When multiple images are considered, the face recognition problem is defined as taking a set of face images from an unknown person and finding the most similar set among the database of labeled image sets. Our proposed method approximates each image set with a geometric convex model (affine/convex hulls) by using the images in these sets. For any pair of models of this form, the distance between them is determined based on the distance between the closest points in these models. By using the kernel trick, the method is extended to the nonlinear case, which allows us to approximate and match complex and nonlinear face image manifolds. The experiments on different databases show that our proposed method outperforms the current state-of-the art methods in many cases.