Joint dynamic sparse representation for multi-view face recognition

Authors:
Haichao Zhang;Nasser M. Nasrabadi;Yanning Zhang;Thomas S. Huang
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China and Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA;U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD, USA;School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China;Beckman Institute, University of Illinois at Urbana-Champaign, IL, USA
Venue:
Pattern Recognition
Year:
2012

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Abstract

We consider the problem of automatically recognizing a human face from its multi-view images with unconstrained poses. We formulate the multi-view face recognition task as a joint sparse representation model and take advantage of the correlations among the multiple views for face recognition using a novel joint dynamic sparsity prior. The proposed joint dynamic sparsity prior promotes shared joint sparsity patterns among the multiple sparse representation vectors at class-level, while allowing distinct sparsity patterns at atom-level within each class to facilitate a flexible representation. Extensive experiments on the CMU Multi-PIE face database are conducted to verify the efficacy of the proposed method.