Expression-invariant face recognition in hyperspectral images

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.