Multiview face recognition based on multilinear decomposition and pose manifold

One major challenge encountered in face recognition is how to handle the wide pose variation and in-depth rotations of head. A multiview face recognition method is proposed in this study that addresses this challenge based on multilinear decomposition approach and pose subspace. In order to preserve the pose manifold geometry among different individuals in pose subspace, a pose-biased distance measure is proposed. In addition, as one of the impediments in manifold-based methods is the lack of sufficient data, a new half-ellipsoid-based pose generation method is presented. For performance evaluation of the proposed multiview face recognition method, three different experiments are run on three famous face datasets. The obtained recognition accuracy and the cumulative match characteristic curves confirm the effectiveness of the proposed method in wide pose variation, even with limited number of training poses.