Performance of correlation filters in facial recognition

Authors:
Everardo Santiago-Ramirez;J. A. Gonzalez-Fraga;J. I. Ascencio-Lopez
Affiliations:
Universidad Autónoma de Baja California, Ensenada, Baja California;Universidad Autónoma de Baja California, Ensenada, Baja California;Universidad Autónoma de Baja California, Ensenada, Baja California
Venue:
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Year:
2011

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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.