用于沉浸式扩展现实（XR）且保护隐私的年龄验证与儿童安全的手背图像
Dorsal Hand Images for Immersive (XR) and Privacy-preserving Age Assurance and Child Safety
- University of Greenwich（格林威治大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对XR环境的年龄验证需求，提出用XR头戴式设备自带相机捕捉的手背图像替代面部，构建数据集验证后实现零未成年人准入，为XR提供了隐私友好的会话内年龄验证方案。
AI中文摘要：
确保扩展现实（XR）环境符合年龄适宜性是一项重要的监管和安全挑战。然而，当前的年龄验证仅在注册时进行，无法在会话期间验证活跃用户的年龄。基于面部的方法是社交媒体和成人平台的主流解决方案，但在XR中不适用，因为它们需要摘下头戴式设备并拍摄自拍照，通常通过移动应用完成，这既会破坏沉浸感，又会带来将面部图片分享给第三方的隐私风险，导致XR平台缺乏可行的持续、会话内且保护隐私的年龄验证路径。我们提出将手背作为面部的替代方案，利用XR头戴式设备固有用于捕捉手势交互的第一人称视角相机。为评估该方案，我们收集了包含436名参与者的按年龄、性别分层且种族多样的数据集，涵盖未成年人与成人的界限，在无约束的光照和方向条件下采集。为明确在未成年人与成人界限处使用现成方法可实现的效果，我们评估了标准神经网络架构在法律关键的18岁阈值下的年龄验证性能。分析确认该性能对肤色变化具有鲁棒性。在该数据集上，challenge-31工作点实现了零未成年人准入，使该系统成为年龄验证的可行第一阶段筛选工具。这些发现表明，手背形态测量学是XR会话内年龄验证的有效且更具隐私保护性的生物识别模态。
英文摘要：
Ensuring that Extended Reality (XR) environments are age-appropriate is an important regulatory and safety challenge. However, current age assurance operates only at registration and cannot verify the age of the active user during a session. Face-based approaches, the dominant solution in social media and adult platforms, are impractical in XR, because they require removing the headset and taking a self-captured image, often on a mobile app. This both breaks immersion and introduces the privacy risk of sharing face pictures with third parties, which leaves XR platforms without a viable path to continuous, in-session and privacy-preserving age assurance. We propose the dorsal part of the hand as an alternative to the face, by exploiting the egocentric cameras that XR headsets inherently and naturally use to capture gesture interactions. To evaluate this, we collect an age- and sex-stratified, ethnodiverse dataset of 436 participants spanning the minor--adult boundary, captured under unconstrained lighting and orientation conditions. To characterise what is achievable with off-the-shelf methods at the minor--adult boundary, we evaluate standard neural network architectures for age assurance at the legally critical 18-year threshold. Analysis confirms performance is robust to skin-tone variation. On this dataset, the challenge-31 operating point achieves zero minor admission, making the system a viable first-stage filter for age assurance. These findings position dorsal hand morphometrics as an effective and more privacy-preserving biometric modality for in-session age assurance in XR.
