  In this paper, we proposed a novel pipeline for image-level classification in
the hyperspectral images. By doing this, we show that the discriminative
spectral information at image-level features lead to significantly improved
performance in a face recognition task. We also explored the potential of
traditional feature descriptors in the hyperspectral images. From our
evaluations, we observe that SIFT features outperform the state-of-the-art
hyperspectral face recognition methods, and also the other descriptors. With
the increasing deployment of hyperspectral sensors in a multitude of
applications, we believe that our approach can effectively exploit the spectral
information in hyperspectral images, thus beneficial to more accurate
classification.
