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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