Using Siamese Neural Networks to Perform Cross-System Behavioral Authentication in Virtual Reality

In this paper, we provide an approach on using behavioral biometrics to perform cross-system high-assurance authentication of users in virtual reality (VR) environments. VR is currently being explored as a critical tool to ensure seamless delivery of essential services, such as education, healthcare, and personal finance, while enabling users to work from home environments. Due to the sensitive nature of personal data generated, VR applications for essential services need to provide secure access. Traditional PIN or password-based credentials can be breached by malicious impostors, or be handed over by an intended user of a VR system to a confederate to assist the intended user in completing a task, e.g., an exam or a physical therapy routine. Existing approaches that use the behavior of the user in VR as a biometric signature fail when users provide enrollment and use-time data on different VR systems. We use Siamese neural networks to learn a distance function that characterizes the systematic differences between data provided across pairs of dissimilar VR systems. Our approach provides average equal error rates (EERs) ranging from 1.38% to 3.86% for authentication using a benchmark dataset that consists of 41 users performing a ball-throwing task with 3 VR systems-an Oculus Quest, an HTC Vive, and an HTC Vive Cosmos. To compare to prior approaches in VR biometrics, we also obtain average accuracies for the task of identification, where given an input user's trajectory in a use-time VR system, we use Siamese networks to return the user with the top matching trajectory in an enrollment VR system as the label. We report identification results ranging from 87.82% to 98.53% with average improvements of 29.78%±8.58% and 30.78%±3.68% over existing approaches that use generic distance matching and fully convolutional networks on the enrollment dataset respectively.