Face classification via sparse approximation

Authors:
Elena Battini Sönmez;Bülent Sankur;Songul Albayrak
Affiliations:
Computer Science Department, Bilgi University, Dolapdere, Istanbul, TR;Electric and Electronic Engineering Department, Bogazici University, Istanbul, TR;Computer Engineering Department, Yildiz Teknik University, Istanbul, TR
Venue:
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
Year:
2011

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Abstract

We address the problem of 2D face classification under adverse conditions. Faces are difficult to recognize since they are highly variable due to such factors as illumination, expression, pose, occlusion and resolution. We investigate the potential of a method where the face recognition problem is cast as a sparse approximation. The sparse approximation provides a significant amount of robustness beneficial in mitigating various adverse effects. The study is conducted experimentally using the Extended Yale Face B database and the results are compared against the Fisher classifier benchmark.