Can gender be predicted from near-infrared face images?
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
A method for robust multispectral face recognition
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
QUEST hierarchy for hyperspectral face recognition
Advances in Artificial Intelligence - Special issue on Machine learning
Nighttime face recognition at long distance: cross-distance and cross-spectral matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Securitas: user identification through RGB-NIR camera pair on mobile devices
Proceedings of the Third ACM workshop on Security and privacy in smartphones & mobile devices
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Matching near-infrared (NIR) face images to visible light (VIS) face images offers a robust approach to face recognition with unconstrained illumination. In this paper we propose a novel method of heterogeneous face recognition that uses a common feature-based representation for both NIR images as well as VIS images. Linear discriminant analysis is performed on a collection of random subspaces to learn discriminative projections. NIR and VIS images are matched (i) directly using the random subspace projections, and (ii) using sparse representation classification. Experimental results demonstrate the effectiveness of the proposed approach for matching NIR and VIS face images.