BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems
BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems
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DOI：
10.3390/s20102944
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发表时间：
2020-05
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影响因子：
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通讯作者：
Ilesanmi Olade;Charles Fleming;Hai-Ning Liang
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文献类型：
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作者：
Ilesanmi Olade;Charles Fleming;Hai-Ning Liang
虚拟现实（VR）发展迅速，并用于许多娱乐和商业目的。需要安全、透明和非侵入性的识别机制，以促进用户的安全参与和安全体验。人在运动学上是独特的，具有个人行为和运动特征，可以在安全敏感的VR应用中利用和使用，以弥补用户无法检测物理世界中潜在的观察攻击者。此外，这种使用用户的人体运动学数据的识别方法在多个用户同时参与VR环境的常见场景中是有价值的。在本文中，我们提出了一项用户研究（n = 15），其中我们的参与者执行了一系列需要物理运动（如抓取，旋转和下降）的受控任务，这些任务可以分解为独特的运动学模式，同时我们在VR环境中监测和捕获他们的手，头和眼睛凝视数据。我们对数据进行了分析，并表明这些数据可以用作使用机器学习分类方法（如kNN或SVM）的高置信度生物识别判别式，从而在识别方面增加了一层安全性，或根据用户的偏好动态调整VR环境。我们还对12名攻击者进行了白盒渗透测试，其中一些人的身体与参与者相似。在初步研究后，我们可以从实际参与者的测试数据中获得平均识别置信度值为0.98，并且训练模型的分类准确率为98.6%。渗透测试表明，所有攻击者的置信度都小于50%（<50%），尽管物理上相似的攻击者具有更高的置信度。这些发现可以帮助设计和开发安全的VR系统。
Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (<50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems.