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

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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.

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Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, 339, 150001, Harbin, People’s Republic of China

    Jun-Bao Li

  2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, D415 Chien-Kung Road, Kaohsiung, 807, Taiwan

    Jeng-Shyang Pan

  3. Visual Information Analysis and Processing Research Center, Harbin Institute of Technology Shenzhen Graduate School, Room 202L, Building No. 4, HIT Campus Shenzhen University Town, 518055, Xili, Shenzhen, People’s Republic of China

    Zhe-Ming Lu

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  1. Jun-Bao Li
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  2. Jeng-Shyang Pan
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  3. Zhe-Ming Lu
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Correspondence to Jun-Bao Li.

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Li, JB., Pan, JS. & Lu, ZM. Face recognition using Gabor-based complete Kernel Fisher Discriminant analysis with fractional power polynomial models. Neural Comput & Applic 18, 613–621 (2009). https://doi.org/10.1007/s00521-009-0272-0

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