Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Video-based Face Recognition via Joint Sparse Representation



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


In video-based face recognition, a key challenge is in exploiting the extra information available in a video. 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 to 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 competitive video-based face recognition algorithms.
NIST Interagency/Internal Report (NISTIR) - 7906
Report Number


Phillips, P. , Patel, V. , Chen, Y. and Chellappa, R. (2013), Video-based Face Recognition via Joint Sparse Representation, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], (Accessed April 19, 2024)
Created January 2, 2013, Updated November 10, 2018