Gait recognition is the process of identifying humans from their bipedal locomotion such as walking or running. As such, gait data is privacy sensitive information and should be anonymized where possible. With the rise of higher quality gait recording techniques, such as depth cameras or motion capture suits, an increasing amount of detailed gait data is captured and processed. Introduction and rise of the Metaverse is but one popular application scenario in which the gait of users is transferred onto digital avatars. As a first step towards developing effective anonymization techniques for high-quality gait data, we study different aspects of movement data to quantify their contribution to gait recognition. We first extract categories of features from the literature on human gait perception and then design experiments for each category to assess how much the information they contain contributes to recognition success. Our results show that gait anonymization will be challenging, as the data is highly redundant and interdependent.
翻译:Gait 识别是一个过程,从行走或运行等双向运动动作中识别人。因此,运动数据是隐私敏感信息,应尽可能匿名。随着深度摄像头或运动捕捉服等更高质量的动作记录技术的上升,收集并处理了大量详细的动作数据。引入和生成Metaverse只是一种流行应用情景,即用户的动作被转移到数字动画上。作为为高质量运动数据开发有效的匿名技术的第一步,我们研究了移动数据的不同方面,以量化其对动作识别的贡献。我们首先从文献中提取了人类动作感知特征的类别,然后为每一类别设计了实验,以评估它们所包含的信息在多大程度上有助于认识成功。我们的结果显示,将用户的动作转移到数字动画上将具有挑战性,因为数据非常冗余和相互依存。