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Proceedings Paper

Expression-invariant face recognition in hyperspectral images
Author(s): Han Wang; Tien C. Bau; Glenn Healey
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Paper Abstract

The performance of a face recognition system degrades when the expression in the probe set is different from the expression in the gallery set. Previous studies use either spatial or spectral information to address this problem. In this paper, we propose an algorithm that uses spatial and spectral information for expression-invariant face recognition. The algorithm uses a set of 3D Gabor filters to exploit spatial and spectral correlations, and a principal-component analysis (PCA) to model expression variation. We demonstrate the effectiveness of the algorithm on a database of 200 subjects.

Paper Details

Date Published: 12 September 2011
PDF: 10 pages
Proc. SPIE 8158, Imaging Spectrometry XVI, 81580Q (12 September 2011); doi: 10.1117/12.896105
Show Author Affiliations
Han Wang, Univ. of California, Irvine (United States)
Tien C. Bau, Univ. of California, Irvine (United States)
Glenn Healey, Univ. of California, Irvine (United States)

Published in SPIE Proceedings Vol. 8158:
Imaging Spectrometry XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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