User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications
User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications
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DOI：
10.3390/virtualworlds1010004
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
S. Asish;Arun K. Kulshreshth;C. Borst
中科院分区：
文献类型：
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作者：
S. Asish;Arun K. Kulshreshth;C. Borst
具有嵌入式眼动跟踪器的新兴虚拟现实（VR）显示器目前正在成为商品硬件（例如，HTC Vive Pro Eye）。眼睛跟踪数据可以用于多种目的，包括注视监测、隐私保护和用户认证/识别。由于安全和隐私问题，识别用户是许多应用程序的组成部分。在本文中，我们将探讨可用于识别用户的方法和眼动跟踪功能。之前的VR研究人员探索了基于运动数据（如身体运动、头部跟踪、眼睛跟踪和手部跟踪数据）的机器学习来识别用户。这类系统通常需要明确的VR任务和许多功能来训练机器学习模型以进行用户识别。我们提出了一个系统来识别用户利用最小的眼睛凝视为基础的功能，而不设计任何识别特定的任务。我们从教育VR应用程序中收集了凝视数据，并使用两种机器学习（ML）模型（随机森林（RF）和k最近邻（kNN））以及两种深度学习（DL）模型（卷积神经网络（CNN）和长短期记忆（LSTM））测试了我们的系统。我们的研究结果表明，ML和DL模型可以识别用户，准确率超过98%，只有六个简单的眼睛注视特征。我们讨论我们的结果，他们对安全和隐私的影响，以及我们工作的局限性。
Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work.