Table of Contents
ISRN Machine Vision
Volume 2013 (2013), Article ID 579126, 10 pages
http://dx.doi.org/10.1155/2013/579126
Research Article

Visible and Infrared Face Identification via Sparse Representation

1LITIS EA 4108-QuantIF Team, University of Rouen, 22 Boulevard Gambetta, 76183 Rouen Cedex, France
2GREYC UMR CNRS 6072 ENSICAEN-Image Team, University of Caen Basse-Normandie, 6 Boulevard Maréchal Juin, 14050 Caen, France

Received 4 April 2013; Accepted 27 April 2013

Academic Editors: O. Ghita, D. Hernandez, Z. Hou, M. La Cascia, and J. M. Tavares

Copyright © 2013 Pierre Buyssens and Marinette Revenu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a facial recognition technique based on facial sparse representation. A dictionary is learned from data, and patches extracted from a face are decomposed in a sparse manner onto this dictionary. We particularly focus on the design of dictionaries that play a crucial role in the final identification rates. Applied to various databases and modalities, we show that this approach gives interesting performances. We propose also a score fusion framework that allows quantifying the saliency classifiers outputs and merging them according to these saliencies.