Abstract

The main challenge in recognizing faces in video is effectively exploiting the multiple frames of a face and the accompanying dynamic signature. One prominent method is based on extracting joint appear- ance and behavioral features. A second method models a person by tem- poral correlations of features in a video. Our approach introduces the concept of video-dictionaries for face recognition, which generalizes the work in sparse representation and dictionaries for faces in still images. Video-dictionaries are designed to implicitly encode temporal, pose, and illumination information. We demonstrate our method on the Face and Ocular Challenge Series (FOCS) Video Challenge, which consists of un- constrained video sequences. We show that our method is efficient and performs significantly better than many competitive video-based face recognition algorithms.