Face Classification via Sparse Approximation - Sonmez, Elena Battini; Sankur, Bulent; Albayrak, Songul (Springer-Verlag Berlin, 2011)
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.
COST 2101 European Workshop on Biometrics and Identity Management (BioID) -- MAR 08-10, 2011 -- Brandenburg Univ Appl Sci, Brandenburg, GERMANY
Keywords: Face Classification; Sparse Approximation; Fisher Classifier; Recognition; Robust; Representation; Illumination