Polynomial Correlation Filters for Human Face Recognition

被引：2

作者：

Alkanhal, Mohamed ^{[1
]}

Muhammad, Ghulam ^{[2
]}

机构：

[1] King Abdulaziz City Sci & Technol, Comp Res Inst, Riyadh, Saudi Arabia

[2] King Saud Univ, Coll Comp & Informat Sci, Riyadh, Saudi Arabia

来源：

2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 1 | 2012年

关键词：

Face recognition; Correlation filters; Distance classifier correlation filter; Nonlinear filters;

D O I：

10.1109/ICMLA.2012.120

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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).

引用

页码：646 / 650

页数：5

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