Impact of Iris Pigmentation on Performance Bias in Visible Iris Verification Systems: A Comparative Study

TL;DR

This study compares how iris pigmentation affects biometric recognition accuracy, revealing biases towards blue irises and emphasizing the need for more inclusive datasets and model improvements to ensure fairness across different iris colors and devices.

Contribution

It provides a comprehensive analysis of pigmentation-related biases in iris recognition systems using multiple models and devices, highlighting the importance of diverse training data.

Findings

Higher accuracy for blue irises compared to dark irises

Training on diverse datasets improves recognition performance

Recognition biases vary across iris colors and devices

Abstract

Iris recognition technology plays a critical role in biometric identification systems, but their performance can be affected by variations in iris pigmentation. In this work, we investigate the impact of iris pigmentation on the efficacy of biometric recognition systems, focusing on a comparative analysis of blue and dark irises. Data sets were collected using multiple devices, including P1, P2, and P3 smartphones [4], to assess the robustness of the systems in different capture environments [19]. Both traditional machine learning techniques and deep learning models were used, namely Open-Iris, ViT-b, and ResNet50, to evaluate performance metrics such as Equal Error Rate (EER) and True Match Rate (TMR). Our results indicate that iris recognition systems generally exhibit higher accuracy for blue irises compared to dark irises. Furthermore, we examined the generalization capabilities of these systems across different iris colors and devices, finding that while training on diverse datasets enhances recognition performance, the degree of improvement is contingent on the specific model and device used. Our analysis also identifies inherent biases in recognition performance related to iris color and cross-device variability. These findings underscore the need for more inclusive dataset collection and model refinement to reduce bias and promote equitable biometric recognition across varying iris pigmentation and device configurations.