Enhancing the correntropy MACE filter with random projections

The minimum average correlation energy (MACE) filter is a well known correlation filter for object recognition. Recently, a nonlinear extension to the MACE filter using the correntropy function in feature space has been introduced. 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 (CMACE) can potentially improve its performance. Both the MACE and CMACE are basically memory-based algorithms and due to the high dimensionality of the image data, the computational cost of the CMACE filter is one of the critical issues in practical applications. We propose to use a dimensionality reduction method based on random projections (RP), which has emerged as a powerful method for dimensionality reduction in machine learning. We apply the CMACE filter with random projection (CMACE-RP) to face recognition and show that it indeed outperforms the traditional linear MACE in both generalization and rejection abilities with small degradation in performance, but great savings in storage and computational complexity.