GazeLock: Gaze- and Lock Pattern-Based Authentication

Password entry is common authentication approach in Extended Reality (XR) applications for its simplicity and familiarity, but it faces challenges in public and dynamic environments due to its cumbersome nature and susceptibility to observation attacks. Manual password input can be disruptive and prone to theft through shoulder surfing or surveillance. While alternative knowledge-based approaches exist, they often require complex physical gestures and are impractical for frequent public use. We present GazeLock, an eye-tracking and lock pattern-based authentication method. This method aims to provide an easy-to-learn and efficient alternative by leveraging familiar lock patterns operated through gaze. It ensures resilience to external observation, as physical interaction is unnecessary and eyes are obscured by the headset. Its hands-free, discreet nature makes it suitable for secure public use. We demonstrate this method by simulating the unlocking of a smart lock via an XR headset, showcasing its potential applications and benefits in real-world scenarios.