Using Motion Forecasting for Behavior-Based Virtual Reality (VR)
  Authentication
Mingjun Li Natasha Kholgade Banerjee Sean Banerjee
Task-based behavioral biometric authentication of users interacting in
virtual reality (VR) environments enables seamless continuous authentication by
using only the motion trajectories of the person's body as a unique signature.
Deep learning-based approaches for behavioral biometrics show high accuracy
when using complete or near complete portions of the user trajectory, but show
lower performance when using smaller segments from the start of the task. Thus,
any systems designed with existing techniques are vulnerable while waiting for
future segments of motion trajectories to become available. In this work, we
present the first approach that predicts future user behavior using
Transformer-based forecasting and using the forecasted trajectory to perform
user authentication. Our work leverages the notion that given the current
trajectory of a user in a task-based environment we can predict the future
trajectory of the user as they are unlikely to dramatically shift their
behavior since it would preclude the user from successfully completing their
task goal. Using the publicly available 41-subject ball throwing dataset of
Miller et al. we show improvement in user authentication when using forecasted
data. When compared to no forecasting, our approach reduces the authentication
equal error rate (EER) by an average of 23.85% and a maximum reduction of
36.14%.