Dictionary-Based Face Recognition from Video

Yi-Chen Chen¹, Vishal M. Patel¹, P. Jonathon Phillips², and Rama Chellappa¹

¹Department of Electrical and Computer Engineering Center for Automation Research, University of Maryland, College Park, MD, USA
chenyc08@umiacs.umd.edu
pvishalm@umiacs.umd.edu
rama@umiacs.umd.edu

²National Institute of Standards and Technology, Gaithersburg, MD, USA
jonathon.phillips@nist.gov

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 appearance and behavioral features. A second method models a person by temporal 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 unconstrained video sequences. We show that our method is efficient and performs significantly better than many competitive video-based face recognition algorithms.

LNCS 7577, p. 766 ff.

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