Manifold Learning for Video-to-Video Face Recognition

We look in this work at the problem of video-based face recognition in which both training and test sets are video sequences, and propose a novel approach based on manifold learning. The idea consists of first learning the intrinsic personal characteristics of each subject from the training video sequences by discovering the hidden low-dimensional nonlinear manifold of each individual. Then, a target face video sequence, is projected and compared to the manifold of each subject. The closest manifold, in terms of a recently introduced manifold distance measure, determines the identity of the person in the sequence. Experiments on a large set of talking faces under different image resolutions show very promising results (recognition rate of 99.8%), outperforming many traditional approaches.