Abstract
Published academic research and media articles suggest face recognition is biased across demographics. Specifically, unequal performance is obtained for women, dark-skinned people, and older adults. However, these published studies have examined the bias of facial recognition in the visible spectrum (VIS). Factors such as facial makeup, facial hair, skin color, and illumination variation have been attributed to the bias of this technology at VIS. The near-infrared (NIR) spectrum offers an advantage over VIS in terms of robustness to factors such as illumination changes, facial make-up, and skin color. Therefore, it is worth-while to investigate the bias of the facial recognition at near-infrared spectrum (NIR). This first study investigates the bias of face recognition system at NIR spectrum. To this aim, two popular NIR facial image datasets namely, CASIA-Face-Africa and NotreDame-NIVL consisting of African and Caucasian subjects, respectively, are used to investigate the bias of facial recognition technology across gender and race. Interestingly, experimental results suggest equitable performance of the face recognition across gender and race at NIR spectrum.