Abstract
The minimum average correlation energy (MACE) filter is a well known correlation filter for pattern recognition. This paper proposes a nonlinear extension to the MACE filter using the recently introduced correntropy function in feature space. Correntropy is a positive definite function that generalizes the concept of correlation by utilizing higher order moment information of signal structure. Since the MACE is a spatial matched filter for an image class, the correntropy MACE can potentially improve its performance. We apply the correntropy MACE filter to face recognition and show that the proposed method indeed outperforms the traditional linear MACE in both generalization and rejection abilities.
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