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Evaluating feature extractors and dimension reduction methods for near infrared face recognition systems

Sheikh, Usman Ullah and Farokhi, Sajad and Flusser, Jan and Shamsuddin, Siti Mariyam and Hashemi, Hossein (2014) Evaluating feature extractors and dimension reduction methods for near infrared face recognition systems. Jurnal Teknologi (Sciences and Engineering), 70 (1). pp. 23-33. ISSN 0127-9696

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Official URL: https://dx.doi.org/10.11113/jt.v70.2459

Abstract

This study evaluates the performance of global and local feature extractors as well as dimension reduction methods in NIR domain. Zernike moments (ZMs), Independent Component Analysis (ICA), Radon Transform + Discrete Cosine Transform (RDCT), Radon Transform + Discrete Wavelet Transform (RDWT) are employed as global feature extractors and Local Binary Pattern (LBP), Gabor Wavelets (GW), Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) are used as local feature extractors. For evaluation of dimension reduction methods Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis + Principal Component Analysis (Fisherface), Kernel Fisher Discriminant Analysis (KFD) and Spectral Regression Discriminant Analysis (SRDA) are used. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that ZMs as a global feature extractor, UDWT as a local feature extractor and SRDA as a dimension reduction method have superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments.

Item Type:Article
Uncontrolled Keywords:face recognition, near infrared, comparative study, Zernike moments, undecimated discrete wavelet transform
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:52749
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:01 Feb 2016 03:54
Last Modified:30 Jun 2018 00:43

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