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.
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Battini Sőnmez, E., Sankur, B., Albayrak, S. (2011). Face Classification via Sparse Approximation. In: Vielhauer, C., Dittmann, J., Drygajlo, A., Juul, N.C., Fairhurst, M.C. (eds) Biometrics and ID Management. BioID 2011. Lecture Notes in Computer Science, vol 6583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19530-3_16
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DOI: https://doi.org/10.1007/978-3-642-19530-3_16
Publisher Name: Springer, Berlin, Heidelberg
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