  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 the VIS. The
near-infrared (NIR) spectrum offers an advantage over the VIS in terms of
robustness to factors such as illumination changes, facial makeup, and skin
color. Therefore, it is worthwhile to investigate the bias of facial
recognition at the near-infrared spectrum (NIR). This first study investigates
the bias of the face recognition systems at the NIR spectrum. To this aim, two
popular NIR facial image datasets namely, CASIA-Face-Africa and Notre-Dame-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 face recognition
performance across gender and race at the NIR spectrum.
