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5 November 2014 Kernel-based discriminant image filter learning: application in face recognition
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Abstract
The extraction of discriminative and robust feature is a crucial issue in pattern recognition and classification. In this paper, we propose a kernel based discriminant image filter learning method (KDIFL) for local feature enhancement and demonstrate its superiority in the application of face recognition. Instead of designing the image filter in a handcraft or analytical way, we propose to learn the image filter so that after filtering the between-class difference is attenuated and the within-class difference is amplified, thus facilitate the following recognition. During filter learning, the kernel trick is employed to cope with the nonlinear feature space problem caused by expression, pose, illumination, and so on. We show that the proposed filter is generalized and it can be concatenated with classic feature descriptors (e.g. LBP) to further increase the discriminability of extracted features. Our extensive experiments on Yale, ORL and AR face databases validate the effectiveness and robustness of the proposed method.
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Lingchen Zhang, Sui Wei, and Lei Qu "Kernel-based discriminant image filter learning: application in face recognition", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92731Q (5 November 2014); https://doi.org/10.1117/12.2073562
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