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

arXiv: https://arxiv.org/abs/2110.01641 · HTML: https://mlsanity.com/paper/2110.01641/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Demographic Bias in Biometrics: A Survey on an Emerging Challenge](https://mlsanity.com/paper/2003.02488/) (2020)
- [Mitigating Face Recognition Bias via Group Adaptive Classifier](https://mlsanity.com/paper/2006.07576/) (2020)
- [Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups](https://mlsanity.com/paper/2009.11491/) (2020)
- [SensitiveNets: Learning Agnostic Representations with Application to Face Images](https://mlsanity.com/paper/1902.00334/) (2019)
- [On the Robustness of Face Recognition Algorithms Against Attacks and Bias](https://mlsanity.com/paper/2002.02942/) (2020)
- [Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images](https://mlsanity.com/paper/2103.16760/) (2021)
- [Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on VISOB 2.0 Dataset](https://mlsanity.com/paper/2011.08898/) (2020)
- [Predicting Gender from Iris Texture May Be Harder Than It Seems](https://mlsanity.com/paper/1811.10066/) (2018)

## Built on by (most cited first, 1 of 1)

- [Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups](https://mlsanity.com/paper/2208.08382/) (2022)

## Abstract (arXiv)

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.