Temporal Effects in Motion Behavior for Virtual Reality (VR) Biometrics

Using the motion behavior of users in virtual reality (VR) as a biometric signature has the potential to enable continuous identification and authentication of users without compromising VR applications if traditional passwords are acquired by malicious agents. Users exhibit natural variabilities in behavior over time that influence their body motions and can alter the trajectories of VR devices such as the headset and the controllers. Behavior variabilities may negatively impact the success rate of VR biometrics. In this work, we evaluate how deep learning approaches to match input and enrollment trajectories are influenced by user behavior variation over varying time scales. We demonstrate that over short timescales on the order of seconds to minutes, no statistically significant relationship is found in the temporal placement of enrollment trajectories and their matches to input trajectories. We find that on medium-scale separation between enrollment and input trajectories, on the order of days to weeks, median accuracy is similar within users who provide input close and distant to enrollment data. Over long timescales on the order of 7 to 18 months, we obtain optimal performance for short and long enrollment/input separations by using training sets from users providing long-timescale data, as these sets encompass coarse and fine-scale changes in behavior.