  A number of studies suggest bias of the face biometrics, i.e., face
recognition and soft-biometric estimation methods, across gender, race, and age
groups. There is a recent urge to investigate the bias of different biometric
modalities toward the deployment of fair and trustworthy biometric solutions.
Ocular biometrics has obtained increased attention from academia and industry
due to its high accuracy, security, privacy, and ease of use in mobile devices.
A recent study in $2020$ also suggested the fairness of ocular-based user
recognition across males and females. This paper aims to evaluate the fairness
of ocular biometrics in the visible spectrum among age groups; young, middle,
and older adults. Thanks to the availability of the latest large-scale 2020
UFPR ocular biometric dataset, with subjects acquired in the age range 18 - 79
years, to facilitate this study. Experimental results suggest the overall
equivalent performance of ocular biometrics across gender and age groups in
user verification and gender classification. Performance difference for older
adults at lower false match rate and young adults was noted at user
verification and age classification, respectively. This could be attributed to
inherent characteristics of the biometric data from these age groups impacting
specific applications, which suggest a need for advancement in sensor
technology and software solutions.
