Current Advances in Hyperspectral Face Recognition

Abstract

Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and increase in the imaging speed has attracted computer vision scientists to apply hyperspectral imaging to ground based computer vision problems such as material classification, agriculture, chemistry and document image analysis. Hyperspectral imaging has also been explored for face recognition; to tackle the issues of pose and illumination variations by exploiting the richer spectral information of hyperspectral images. In this article, we present a detailed review on the potential of hyperspectral imaging for face recognition. We present hyperspectral image aquisition process and discuss key preprocessing challenges. We also discuss hyperspectral face recognition databases and techniques for feature extraction from the hyperspectral images. Potential future research directions are also highlighted

Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios

“…However, apart from the spectral information captured by hyperspectral sensors, it complements the data information collected by traditional sensors, such as RGB cameras. These kinds of sensors have improved over the last decade by reductions in their cost and increases in imaging speed, which in turn has opened up hyperspectral imaging to other applications, and making it more popular than ever in recent decades [ 5 , 6 ]. Hyperspectral imaging is widely used for a large variety of applications such as precision agriculture, forestry, city planning, urban surveillance and homeland security, chemistry, forensic examination and face recognition.…”