SIAM Journal on Scientific and Statistical Computing
A nonlinear extension of the MACE filter
Neural Networks - Special issue: automatic target recognition
Efficient Design of Advanced Correlation Filters for Robust Distortion-Tolerant Face Recognition
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Correlation Pattern Recognition
Correlation Pattern Recognition
Kernel correlation filter based redundant class-dependence feature analysis (KCFA) on FRGC2.0 data
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
Generalized correlation function: definition, properties, and application to blind equalization
IEEE Transactions on Signal Processing - Part I
Signal detection by complex spatial filtering
IEEE Transactions on Information Theory
A test of independence based on a generalized correlation function
Signal Processing
A regularized correntropy framework for robust pattern recognition
Neural Computation
The C-loss function for pattern classification
Pattern Recognition
Pattern Recognition Letters
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The minimum average correlation energy (MACE) filter is well known for object recognition. This paper proposes a nonlinear extension to the MACE filter using the recently introduced correntropy function. Correntropy is a positive definite function that generalizes the concept of correlation by utilizing second and higher order moments of the signal statistics. Because of its positive definite nature, correntropy induces a new reproducing kernel Hilbert space (RKHS). Taking advantage of the linear structure of the RKHS it is possible to formulate the MACE filter equations in the RKHS induced by correntropy and obtained an approximate solution. Due to the nonlinear relation between the feature space and the input space, the correntropy MACE (CMACE) can potentially improve upon the MACE performance while preserving the shift-invariant property (additional computation for all shifts will be required in the CMACE). To alleviate the computation complexity of the solution, this paper also presents the fast CMACE using the fast Gauss transform (FGT). We apply the CMACE filter to the MSTAR public release synthetic aperture radar (SAR) data set as well as PIE database of human faces and show that the proposed method exhibits better distortion tolerance and outperforms the linear MACE in both generalization and rejection abilities.