Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults

TL;DR

This study evaluates the fairness of ocular biometrics across different age groups, finding generally equivalent performance but noting some differences that highlight the need for technological improvements.

Contribution

It provides a large-scale analysis of ocular biometric fairness across age groups, filling a gap in existing research focused mainly on gender and race.

Findings

Ocular biometrics show overall fairness across age groups.

Performance differences observed at specific false match rates.

Older adults and young adults exhibit some variability in verification and classification.

Abstract

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.