GazePair: Efficient Pairing of Mixed Reality Devices Using Gaze Tracking

Mixed reality (MR) devices, encompassing both augmented and virtual reality technologies, are rapidly gaining traction for their potential to revolutionize various sectors, including gaming, healthcare, education, logistics, and military applications. As these devices become increasingly integrated into our daily lives, the need for seamless and secure pairing mechanisms becomes paramount. Secure pairing is crucial for establishing trusted connections between devices, enabling collaborative experiences, and ensuring the secure exchange of sensitive information, such as holograms and spatial data. Current approaches to pairing MR devices often fall short in terms of usability, security, and scalability. Some methods rely on complex physical gestures or require specialized hardware not commonly available on MR devices. Others suffer from low entropy generation, making them susceptible to brute-force attacks. Moreover, existing solutions often lack scalability, limiting their practicality for multi-user scenarios. These limitations hinder the widespread adoption of MR technologies and underscore the need for more robust and user-friendly pairing solutions.

GazePair technology facilitates secure pairing between multiple mixed-reality devices using gaze tracking. It identifies a host and at least one client device, communicating a key sequence cue to the user via the host device. Simultaneously, the host places ten numerically labeled holograms. Gaze tracking sensors monitor the users' eye movements as they follow a sequence of numbers corresponding to the spoken cue. This sequence generates a cryptographic key, establishing secure communication by leveraging the natural interaction of eye gaze to create a shared secret.

What differentiates this technology is its innovative use of gaze tracking for secure pairing in mixed reality. The system boasts a 98.5% success rate for one-to-one pairings and 100% for one-to-two pairings, demonstrating both efficiency and scalability. Moreover, the system has a low average pairing time, 8.9 seconds to complete one-to-one pairing and 12.4 seconds for one-to-two pairings achieving an 82.6 on a System Usability Scale (SUS) which is deemed “excellent”. Additionally, the efficiency increased over time as more users familiarized with the system quickly displaying a shorter learning curve amongst the users. Its adaptability to various mixed-reality platforms with gaze tracking capabilities further enhances its potential for widespread adoption.