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.

翻译：虹膜识别技术在生物特征识别系统中发挥着关键作用，但其性能可能受虹膜色素沉着变化的影响。本研究探讨了虹膜色素沉着对生物特征识别系统效能的影响，重点对蓝色与深色虹膜进行了比较分析。通过使用包括P1、P2和P3智能手机在内的多种设备采集数据集，以评估系统在不同采集环境中的鲁棒性。研究采用传统机器学习技术和深度学习模型（包括Open-Iris、ViT-b和ResNet50）评估了等错误率与真实匹配率等性能指标。结果表明，与深色虹膜相比，虹膜识别系统对蓝色虹膜通常表现出更高的准确率。此外，我们检验了这些系统在不同虹膜颜色和设备间的泛化能力，发现尽管基于多样化数据集的训练能提升识别性能，但改进程度取决于具体模型与设备。我们的分析还识别出与虹膜颜色及跨设备变异性相关的识别性能固有偏差。这些发现强调，需要通过更具包容性的数据集收集和模型优化来减少偏差，促进不同虹膜色素沉着与设备配置间的公平生物特征识别。