In recent years, sparse representation and dictionary-learning-based methods have emerged as powerful tools for efficiently processing data in nontraditional ways. A particular area of promise for these theories is face recognition. In this paper, we review the role of sparse representation and dictionary learning for efficient face identification and verification. Recent face recognition algorithms from still images, videos, and ambiguously labeled imagery are reviewed. In particular, discriminative dictionary learning algorithms as well as methods based on weakly supervised learning and domain adaptation are summarized. Some of the compelling challenges and issues that confront research in face recognition using sparse representations and dictionary learning are outlined.
© 2014 Optical Society of America
Original Manuscript: January 6, 2014
Revised Manuscript: March 16, 2014
Manuscript Accepted: March 18, 2014
Published: April 25, 2014
Vishal M. Patel, Yi-Chen Chen, Rama Chellappa, and P. Jonathon Phillips, "Dictionaries for image and video-based face recognition [Invited]," J. Opt. Soc. Am. A 31, 1090-1103 (2014)