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Journal of Electronic Imaging

Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis
Author(s): Sajad Farokhi; Siti Mariyam Shamsuddin; Jan Flusser; Usman Ullah Sheikh; Mohammad Khansari; Kourosh Jafari-Khouzani
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Paper Abstract

Face recognition is a rapidly growing research area, which is based heavily on the methods of machine learning, computer vision, and image processing. We propose a rotation and noise invariant near-infrared face-recognition system using an orthogonal invariant moment, namely, Zernike moments (ZMs) as a feature extractor in the near-infrared domain and spectral regression discriminant analysis (SRDA) as an efficient algorithm to decrease the computational complexity of the system, enhance the discrimination power of features, and solve the “small sample size” problem simultaneously. Experimental results based on the CASIA NIR database show the noise robustness and rotation invariance of the proposed approach. Further analysis shows that SRDA as a sophisticated technique, improves the accuracy and time complexity of the system compared with other data reduction methods such as linear discriminant analysis.

Paper Details

Date Published: 1 March 2013
PDF: 11 pages
J. Electron. Imaging. 22(1) 013030 doi: 10.1117/1.JEI.22.1.013030
Published in: Journal of Electronic Imaging Volume 22, Issue 1
Show Author Affiliations
Sajad Farokhi, Univ. Teknologi Malaysia (Malaysia)
Siti Mariyam Shamsuddin, Univ. Teknologi Malaysia (Malaysia)
Jan Flusser, Institute of Information Theory and Automation (Czech Republic)
Usman Ullah Sheikh, Univ. Teknologi Malaysia (Malaysia)
Mohammad Khansari, Univ. of Tehran (Iran, Islamic Republic of)
Kourosh Jafari-Khouzani


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