Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition
Abstract
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.
Cite
@article{arxiv.1605.03428,
title = {Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition},
author = {Vivek Sharma and Luc Van Gool},
journal= {arXiv preprint arXiv:1605.03428},
year = {2016}
}