Expression-invariant face recognition in hyperspectral images

Han Wang; Tien C. Bau; Glenn E. Healey

doi:10.1117/1.OE.53.10.103102

3 October 2014 Expression-invariant face recognition in hyperspectral images

Han Wang, Tien C. Bau, Glenn E. Healey

Author Affiliations +

Optical Engineering, Vol. 53, Issue 10, 103102 (October 2014). https://doi.org/10.1117/1.OE.53.10.103102

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. We propose an algorithm that uses spatial and spectral information for expression-invariant face recognition. The algorithm uses a set of three-dimensional Gabor filters to exploit spatial and spectral correlations, while principal-component analysis is used to model expression variation. We demonstrate the effectiveness of the algorithm on a database of 200 subjects with neutral and smiling expressions and explore the dependence of the performance on image spatial resolution and training set size.

Citation Download Citation

Han Wang, Tien C. Bau, and Glenn E. Healey "Expression-invariant face recognition in hyperspectral images," Optical Engineering 53(10), 103102 (3 October 2014). https://doi.org/10.1117/1.OE.53.10.103102

Published: 3 October 2014

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