  Recent research has questioned the fairness of face-based recognition and
attribute classification methods (such as gender and race) for dark-skinned
people and women. Ocular biometrics in the visible spectrum is an alternate
solution over face biometrics, thanks to its accuracy, security, robustness
against facial expression, and ease of use in mobile devices. With the recent
COVID-19 crisis, ocular biometrics has a further advantage over face biometrics
in the presence of a mask. However, fairness of ocular biometrics has not been
studied till now. This first study aims to explore the fairness of ocular-based
authentication and gender classification methods across males and females. To
this aim, VISOB $2.0$ dataset, along with its gender annotations, is used for
the fairness analysis of ocular biometrics methods based on ResNet-50,
MobileNet-V2 and lightCNN-29 models. Experimental results suggest the
equivalent performance of males and females for ocular-based mobile
user-authentication in terms of genuine match rate (GMR) at lower false match
rates (FMRs) and an overall Area Under Curve (AUC). For instance, an AUC of
0.96 for females and 0.95 for males was obtained for lightCNN-29 on an average.
However, males significantly outperformed females in deep learning based gender
classification models based on ocular-region.
