Video face recognition via combination of real-time local features and temporal-spatial cues
Video-based face recognition has attracted much attention and made great progress in the past decade. However, it still encounters two main problems, which are efficiently representing faces in frames and sufficiently exploiting temporal–spatial constraints between frames. The authors investigate the existing real-time features for face description, and compare their performance. Moreover, a novel approach is proposed to model temporal–spatial information which is then combined with real-time features to further enforce the consistent constraints between frames to improve the recognition performance. The experiments are validated on three video face databases and the results demonstrate that temporal–spatial cues combined with the most powerful real-time features largely improve the recognition rate.