针对虚拟现实头显的虹膜认证难题，提出多模态融合新方案。
Ocular Verification for Virtual Reality
- 用生成模型缓解视角偏移、光照不均等VR特有问题
- 多模态融合使错误率降低约11%
- 公开评估代码，支持复现验证
虚拟现实头显（如Meta Quest、Apple Vision Pro）依赖虹膜等生物特征进行用户认证。然而，传统虹膜识别标准在非受控采集场景下表现不足，而这类场景正是VR数据的典型特征。本文研究三个关键问题：(1) 评估ISO/IEC 29794-6虹膜质量指标在VRBiom数据集上的适用性并分析其局限；(2) 利用生成模型应对离轴注视、非均匀光照和镜面反射等数据特定挑战；(3) 实现虹膜与眼周区域的单模态识别及评分级融合。结果表明，部分质量指标（如边缘充足性）在VR数据上失效；图像调整主要提升眼周识别性能；多模态融合相较单模态虹膜识别将等错误率（EER）降低约11%。论文将在接受后发布评估脚本以保障可复现性。
原文摘要 · Abstract (English)
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.
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