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Performance of Correlation Filters in Facial Recognition

机译:相关滤波器在面部识别中的性能

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摘要

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.
机译:在本文中,我们比较了三种复合相关滤波器在面部识别问题中的性能。我们使用ORL(Olivetti研究实验室)面部图像数据库来评估K-Law,MACE和ASEF滤镜性能。仿真结果表明,K-Law非线性复合滤波器在识别率(RR)和错误接受率(FAR)方面表现出最佳性能。结果,我们观察到,即使面部图像包含诸如旋转,部分遮挡和不同的照明条件之类的失真,相关滤镜也能很好地工作。

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