Quality Fusion Rule for Face Recognition in Video

Face recognition in video is confronted with many problems: varying illumination, pose and expression. Their compensation algorithms may produce much noise and make face abnormal, which degrade the face image quality. In this paper, motivated by human cognitive process, a quality fusion rule is designed to reduce the influence of compensated face image quality that may affect recognition performance. Combined with video features and the recognition contribution degrees of compensated face image, the rule fuses the recognition result of every face video frame to opt best result. In this paper, quality fusion rule for illumination compensation is mainly involved. In the experiment, the proposed quality fusion rule is evaluated on a face video database with varied illumination. In contrast to other state-of-the-art methods, the novel approach has better recognition performance.