Prototype discriminative learning for face image set classification

Abstract

This paper presents a novel Prototype Discriminative Learning (PDL) method to solve the problem of face image set classification. We aim to simultaneously learn a set of prototypes for each image set and a linear discriminative transformation to make projections on the target subspace satisfy that each image set can be optimally classified to the same class with its nearest neighbor prototype. For an image set, its prototypes are actually “virtual” as they do not certainly appear in the set but are only assumed to belong to the corresponding affine hull, i.e., affine combinations of samples in the set. Thus, the proposed method not only inherits the merit of classical affine hull in revealing unseen appearance variations implicitly in an image set, but more importantly overcomes its flaw caused by too loose affine approximation via efficiently shrinking each affine hull with a set of discriminative prototypes. The proposed method is evaluated by face identification and verification tasks on three challenging and large-scale databases, YouTube Celebrities, COX and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.