用可见虹膜面积评估虹膜图像质量，提升识别可靠性。
Visible Iris Area as a Quality Metric for Reliable Iris Recognition Under Pupil Dilation and Eyelid Occlusion
- 以可见虹膜面积作为质量指标，实时判断图像可用性。
- 在555个虹膜数据上验证，可见面积与哈明距离强相关。
- 适合用于真实场景中应对瞳孔放大和眼睑遮挡的系统优化。
随着虹膜识别系统广泛应用及大规模注册数据库的发展，实时评估虹膜图像质量的需求日益迫切，尤其需要建模用户非配合情况。图像质量可能因眼睑遮挡或瞳孔扩张而下降。尽管先前研究已表明遮挡和瞳孔-虹膜比例变化会负面影响识别性能，但这些研究通常样本量小，且未考察眼睑与瞳孔变化的联合影响。本研究基于包含555个不同虹膜的大规模数据集，分析了瞳孔扩张与眼睑遮挡的影响，发现探测图像的可见虹膜面积与虹膜码对的哈明距离存在显著相关性。结果表明，可见虹膜面积是探测图像质量的稳健指标，可高效融入虹膜采集流程，提升匹配预测的置信度。
原文摘要 · Abstract (English)
With the increasing adoption of iris recognition systems and the expansion of large-scale enrollment databases, there is a growing need to efficiently assess iris image quality at the time of acquisition, particularly to model user non-compliance in real time. Image quality may degrade due to eyelid occlusion or pupil dilation. Although previous studies have shown that occlusion and changes in the pupil-to-iris ratio negatively impact recognition performance, these investigations were typically limited by small sample sizes and did not examine the combined effects of eyelid and pupil variations. In this study, we analyze both dilation and eyelid occlusion using a large dataset of 555 distinct irises and demonstrate a strong correlation between probe image visible iris area and the Hamming distance of iris code pairs. These results suggest that visible iris area is a robust indicator of probe image quality and could be efficiently incorporated into the iris acquisition process to improve confidence in match predictions.
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