Abstract
In this paper, we compare the performance of three composite correlation filters in facial recognition problem. We used the ORL (Olivetti Research Laboratory) facial image database to evaluate K-Law, MACE and ASEF filters performance. Simulations results demonstrate that K-Law nonlinear composite filters evidence the best performance in terms of recognition rate (RR) and, false acceptation rate (FAR). As a result, we observe that correlation filters are able to work well even when the facial image contains distortions such as rotation, partial occlusion and different illumination conditions.
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Santiago-Ramirez, E., Gonzalez-Fraga, J.A., Ascencio-Lopez, J.I. (2011). Performance of Correlation Filters in Facial Recognition. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds) Pattern Recognition. MCPR 2011. Lecture Notes in Computer Science, vol 6718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21587-2_17
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DOI: https://doi.org/10.1007/978-3-642-21587-2_17
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