12 September 2011 Expression-invariant face recognition in hyperspectral images
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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.
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Han Wang, Han Wang, Tien C. Bau, Tien C. Bau, Glenn Healey, Glenn Healey, } "Expression-invariant face recognition in hyperspectral images", Proc. SPIE 8158, Imaging Spectrometry XVI, 81580Q (12 September 2011); doi: 10.1117/12.896105; https://doi.org/10.1117/12.896105

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