标题： 面部运动ID：使用抽象面部运动表示识别混合现实头显用户 显示英文标题

标题： FacialMotionID: Identifying Users of Mixed Reality Headsets using Abstract Facial Motion Representations

摘要： 混合现实头显中的面部动作捕捉技术实现了实时虚拟角色动画，使用户能够在虚拟交互中传达非语言线索。 然而，由于面部动作数据属于行为生物特征，其使用引发了新的隐私担忧。 随着混合现实系统的沉浸感和普及度不断提高，了解面部动作数据是否可能导致用户识别或推断出敏感属性变得越来越重要。 为了解决这一问题，我们进行了一个包含116名参与者的实验，使用三种类型的头显进行三次会话，收集用户在口头和非口头任务中的面部、眼睛和头部动作数据。 所使用的数据不是原始视频，而是用于动画数字角色的抽象表示。 我们的分析表明，可以通过这些数据以高达98%的平衡准确率重新识别个人，甚至可以在不同设备类型之间识别，并且可以以高达86%的准确率推断情绪状态。 这些结果强调了在混合现实环境中面部动作跟踪潜在的隐私风险。 显示英文摘要

摘要： Facial motion capture in mixed reality headsets enables real-time avatar animation, allowing users to convey non-verbal cues during virtual interactions. However, as facial motion data constitutes a behavioral biometric, its use raises novel privacy concerns. With mixed reality systems becoming more immersive and widespread, understanding whether face motion data can lead to user identification or inference of sensitive attributes is increasingly important. To address this, we conducted a study with 116 participants using three types of headsets across three sessions, collecting facial, eye, and head motion data during verbal and non-verbal tasks. The data used is not raw video, but rather, abstract representations that are used to animate digital avatars. Our analysis shows that individuals can be re-identified from this data with up to 98% balanced accuracy, are even identifiable across device types, and that emotional states can be inferred with up to 86% accuracy. These results underscore the potential privacy risks inherent in face motion tracking in mixed reality environments.