Video-based Face Recognition via Joint Sparse Representation

Published

April 26, 2013

Author(s)

P J. Phillips, Yi-Chen Chen, Vishal M. Patel, Sumit Shekar, Rama Chellappa

Abstract

In video-based face recognition, a key challenge is in exploiting the extra information available in a video; e.g., face, body, and motion identity cues. In addition, different video sequences of the same subject may contain variations in resolution, illumination, pose, and facial expressions. These variations contribute to the challenges in designing an effective video-based face-recognition algorithm. We propose a novel multivariate sparse representation method for video-to-video face recognition. Our method simultaneously takes into account correlations as well as coupling information among the video frames. Our method jointly represents all the video data by a sparse linear combination of training data. In addition, we modify our model so that it is robust in the presence of noise and occlusion. Furthermore, we kernelize the algorithm to handle the non-linearities present in video data. Numerous experiments using unconstrained video sequences show that our method is effective and performs significantly better than many state-of- the-art video-based face recognition algorithms in the literature.

Proceedings Title

Tenth IEEE Conference on Automatic Face and Gesture Recognition

Conference Dates

April 22-26, 2013

Conference Location

Shanghai

Pub Type

Conferences

Download Paper

Local Download

Citation

Phillips, P. , Chen, Y. , Patel, V. , Shekar, S. and Chellappa, R. (2013), Video-based Face Recognition via Joint Sparse Representation, Tenth IEEE Conference on Automatic Face and Gesture Recognition, Shanghai, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=913142 (Accessed October 5, 2024)