Face recognition using Gabor-based complete Kernel Fisher Discriminant analysis with fractional power polynomial models

Authors:
Jun-Bao Li;Jeng-Shyang Pan;Zhe-Ming Lu
Affiliations:
Harbin Institute of Technology, Department of Automatic Test and Control, 339, 150001, Harbin, People’s Republic of China;National Kaohsiung University of Applied Sciences, Department of Electronic Engineering, D415 Chien-Kung Road, 807, Kaohsiung, Taiwan;Harbin Institute of Technology Shenzhen Graduate School, Visual Information Analysis and Processing Research Center, Room 202L, Building No. 4, HIT Campus Shenzhen University Town, 518055, Xili, S ...
Venue:
Neural Computing and Applications
Year:
2009

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Abstract

This paper presents a novel face recognition method by integrating the Gabor wavelet representation of face images and the enhanced powerful discriminator, complete Kernel Fisher Discriminant (CKFD) with fractional power polynomial (FPP) models. The novelty of this paper comes from (1) Gabor wavelet, is employed to extract desirable facial features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations in illumination and facial expressions, which improves the recognition performance; (2) a recently proposed powerful discriminator, namely CKFD, which enhances its discriminating ability using two kinds of discriminant information (i.e., regular and irregular information), is employed to classify the Gabor features; (3) the FPP models, are employed to CKFD analysis to enhance the discriminating ability. Comparing with existing principal component analysis, linear discriminant analysis, kernel principal component analysis, KFD and CKFD methods, the proposed method gives the superior results with the ORL, Yale and UMIST face databases.