Two-directional two-dimensional modified Fisher principal component analysis: an efficient approach for thermal face verification

Ning Wang; Qiong Li; Ahmed A. Abd El-Latif; Jialiang Peng; Xiamu Niu

doi:10.1117/1.JEI.22.2.023013

10 May 2013 Two-directional two-dimensional modified Fisher principal component analysis: an efficient approach for thermal face verification

Ning Wang, Qiong Li, Ahmed A. Abd El-Latif, Jialiang Peng, Xiamu Niu

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 2, 023013 (May 2013). https://doi.org/10.1117/1.JEI.22.2.023013

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.

Citation Download Citation

Ning Wang, Qiong Li, Ahmed A. Abd El-Latif, Jialiang Peng, and Xiamu Niu "Two-directional two-dimensional modified Fisher principal component analysis: an efficient approach for thermal face verification," Journal of Electronic Imaging 22(2), 023013 (10 May 2013). https://doi.org/10.1117/1.JEI.22.2.023013

Published: 10 May 2013

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