Video-based face recognition based on view synthesis from 3D face model reconstructed from a single image

Most of the face recognition algorithms were proposed based on training numerous still examples of face images to accommodate different face variations, such as pose and illumination variations. However, it is not practical to collect lots of face images under different variations for each subject in a real authentication system. In this paper, we propose a novel face recognition system with only one single image for each individual in the training dataset. The proposed face recognition system applies the 3D face model reconstructed from the single face to synthesize different views for effectively training, thus leading to robustness against poses variations. The proposed system integrates the temporal face recognition results from the video in a probabilistic framework to make reliable decision when enough evidence is accumulated. In addition, it rejects imposters with the notion of locally linear embedding. The experiment results on FG-Net video database are shown to validate the effectiveness and reliability of the proposed algorithm.