Polynomial Correlation Filters for Human Face Recognition

Abstract

This paper describes a nonlinear face recognition method based on polynomial spatial frequency image processing. This nonlinear method is known as the polynomial distance classifier correlation filter (PDCCF). PDCCF is a member of a well-known family of filters called correlation filters. Correlation filters are attractive because of their shift invariance and potential for distortion tolerant pattern recognition. PDCCF addresses more than one filter in the system, each one with a different form of non-linearity. Our experimental results on the Olivetti Research Laboratory (ORL) and Extended Yale B (EYB) face datasets show that PDCCF outperforms the principal component analysis (PCA), and the local binary pattern (LBP).

Robust face recognition via non‐linear correlation filter bank

“…In [18], non-linear composite filters for distortion invariant pattern recognition were proposed. Alkanhal proposed a non-linear face recognition method based on the polynomial distance classifier CF in spatial frequency image processing [19]. Correntropy MACE (CMACE) is a non-linear form of MACE which introduced second-and higher-order moments of signal statistics to enhance the CMACE discrimination [20].…”