发表机构
University of Wyoming; San Diego State University（怀俄明大学; 圣地亚哥州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究虚拟现实中眼部验证，评估虹膜质量指标局限性，用生成模型应对数据挑战，进行单模态与多模态识别及融合，发现部分指标在VR数据上失效，图像调整利于眼周识别，多模态融合降低错误接受率。
AI 中文摘要
虚拟现实（VR）头显（如Meta Quest、Apple Vision Pro）在模拟环境中与物理世界进行快速、无摩擦交互，提供无缝用户体验。用户认证依赖生物特征线索如虹膜。但传统虹膜识别协议在无约束采集情况下可能不足，这在基于VR的数据中很典型。本文研究三个关键方面：在VRBiom数据集上评估ISO/IEC 29794-6虹膜质量指标并分析其局限性；使用生成模型应对如离轴注视、非均匀照明和镜面反射等特定数据挑战；进行单模态（虹膜、眼周）识别和多模态分数级融合（虹膜+眼周）。观察到一些指标在VR采集数据上失败；图像调整主要有益于眼周识别，多模态融合比单模态虹膜识别性能降低错误接受率约11%。接受后将发布评估脚本以实现可重复性。
英文摘要
Virtual reality (VR) headsets (e.g., Meta Quest, Apple Vision Pro) provide a seamless user experience due to their fast, frictionless interaction with the physical world in a simulated environment. User authentication relies on biometric cues such as iris in such headsets. However, traditional iris recognition protocols may not be adequate in cases of unconstrained acquisition, which is typical of VR-based data. In this work, we examine three crucial aspects: (1) evaluating ISO/IEC 29794-6 iris quality metrics on VRBiom dataset and analyzing their limitations, (2) addressing data-specific challenges such as off-axis gaze, non-uniform illumination, and specular reflection using generative models, and (3) performing unimodal (iris, periocular) recognition and multimodal score-level fusion (iris + periocular). We observe that some metrics (e.g., margin adequacy) fail on VR-acquired data; whereas, image adjustments primarily benefit periocular recognition, and multimodal fusion lowers EER by ~11% over unimodal iris recognition performance. We will release the evaluation scripts upon acceptance for reproducibility.
