Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition
This work addresses face recognition using hyperspectral images, offering improved accuracy for applications deploying hyperspectral sensors, but it appears incremental as it applies traditional descriptors to a new data type.
The paper tackled image-level classification in hyperspectral images by proposing a novel pipeline that leverages discriminative spectral information, resulting in SIFT features outperforming state-of-the-art methods in face recognition tasks.
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.