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Proceedings Paper

Infrared face recognition using linear subspace analysis
Author(s): Wei Ge; Dawei Wang; Yuqi Cheng; Ming Zhu
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Paper Abstract

Infrared image offers the main advantage over visible image of being invariant to illumination changes for face recognition. In this paper, based on the introduction of main methods of linear subspace analysis, such as Principal Component Analysis (PCA) , Linear Discriminant Analysis(LDA) and Fast Independent Component Analysis (FastICA),the application of these methods to the recognition of infrared face images offered by OTCBVS workshop are investigated, and the advantages and disadvantages are compared. Experimental results show that the combination approach of PCA and LDA leads to better classification performance than single PCA approach or LDA approach, while the FastICA approach leads to the best classification performance with the improvement of nearly 5% compared with the combination approach.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961Z (30 October 2009); doi: 10.1117/12.832984
Show Author Affiliations
Wei Ge, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Graduate School of the Chinese Academy of Sciences (China)
Dawei Wang, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Graduate School of the Chinese Academy of Sciences (China)
Yuqi Cheng, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Graduate School of the Chinese Academy of Sciences (China)
Ming Zhu, Changchun Institute of Optics, Fine Mechanics and Physics (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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