Knowledge-driven Biometric Authentication in Virtual Reality

With the increasing adoption of virtual reality (VR) in public spaces, protecting users from observation attacks is becoming essential to prevent attackers from accessing context-sensitive data or performing malicious payment transactions in VR. In this work, we propose RubikBiom, a knowledge-driven behavioural biometric authentication scheme for authentication in VR. We show that hand movement patterns performed during interactions with a knowledge-based authentication scheme (e.g., when entering a PIN) can be leveraged to establish an additional security layer. Based on a dataset gathered in a lab study with 23 participants, we show that knowledge-driven behavioural biometric authentication increases security in an unobtrusive way. We achieve an accuracy of up to 98.91% by applying a Fully Convolutional Network (FCN) on 32 authentications per subject. Our results pave the way for further investigations towards knowledge-driven behavioural biometric authentication in VR

Human Performance Using Computer Input Devices in the Preferred and

Subjects ’ performance was compared in pointing and dragging tasks using the preferred and non-preferred hands. Tasks were tested using three different input devices: a mouse, a trackball, and a tablet-with-stylus. The trackball had the least degradation across hands in performing the tasks, however it remained inferior to both the mouse and stylus. For small distances and small targets, the preferred hand was superior. However, for larger targets and larger distances, both hands performed about the same. The experiment shows that the non-pteferred hand is more than a poor approximation of the preferred hand. The hands are complementary, each having its own strength and weakness. One design implication is that the non-preferred hand is well suited for tasks that do not require precise action, such as scrolling. KEYWORDS: Hand comparisons, computer input, Fitts’ law