Hyperspectral CNN for image classification & band selection, with application to face recognition

With hyperspectral sensor technology evolving and becoming more cost-effective, it is likely we will see hyperspectral cameras replace standard RGB cameras in a multitude of applications beyond these traditional niches of medical and aerial image segmentation in the near future. Rather than generating an image that is optimal for the human eye, responses of these new cameras will be tuned towards specific computer vision algorithms. This calls for new methods for hyperspectral band selection, optimized for those tasks. In this work, we present a novel pipeline for discriminative band selection in hyperspectral images for the task of image-level classification. It is based on a convolutional neural network to learn appropriate representations combined with AdaBoostSVM for band selection. We test our method on two standard hyperspectral face datasets in the context of face recognition. Our exhaustive experiments show that the proposed method outperforms the existing state-of-the-art methods.