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Journal of Electronic Imaging

Two-directional two-dimensional modified Fisher principal component analysis: an efficient approach for thermal face verification
Author(s): Ning Wang; Qiong Li; Ahmed A. Abd El-Latif; Jialiang Peng; Xiamu Niu
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Paper Abstract

In recent years, verification based on thermal face images has been extensively studied because of its invariance to illumination and immunity to forgery. However, most of them have not given full consideration to high-verification performance and singular within-class scatter matrix problems. We propose a novel thermal face verification algorithm, which is named two-directional two-dimensional modified Fisher principal component analysis. First, two-dimensional principal component analysis (2-DPCA) is utilized to extract the optimal projective vector in the row direction. Then, 2-D modified Fisher linear discriminant analysis is implemented to overcome the singular within-class scatter matrix problem of the 2-DPCA space in the column direction. Comparative experiments on the natural visible and infrared facial expression thermal face subdatabase demonstrate that the proposed approach outperforms state-of-the-art methods in terms of verification performance.

Paper Details

Date Published: 10 May 2013
PDF: 8 pages
J. Electron. Imag. 22(2) 023013 doi: 10.1117/1.JEI.22.2.023013
Published in: Journal of Electronic Imaging Volume 22, Issue 2
Show Author Affiliations
Ning Wang, Harbin Institute of Technology (China)
Qiong Li, Harbin Institute of Technology (China)
Ahmed A. Abd El-Latif, Harbin Institute of Technology (Egypt)
Jialiang Peng, Harbin Institute of Technology (China)
Xiamu Niu, Harbin Institute of Technology (China)

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