Self-Organization of Viewpoint Dependent Face Representation by the Self-Supervised Learning and Viewpoint Independent Face Recognition by the Mixture of Classifiers

This paper proposes a viewpoint invariant face recognition method in which several viewpoint dependent classifiers are combined by a gating network. The gating network is designed as autoencoder with competitive hidden units. The viewpoint dependent representations of faces can be obtained by this autoencoder from many faces with different views. Multinomial logit model is used for the viewpoint dependent classifiers. By combining the classifiers with the gating network, the network can be self-organized such that one of the classifiers is selected depending on the viewpoint of a given input face image. Experimental results of view invariant face recognition are shown using the face images c a p tured from different viewpoints.