Eye Tracking Interaction on Unmodified Mobile VR Headsets Using the Selfie Camera

Drakopoulos, Panagiotis; Koulieris, George-Alex; Mania, Katerina

Abstract

Input methods for interaction in smartphone-based virtual and mixed reality (VR/MR) are currently based on uncomfortable head tracking controlling a pointer on the screen. User fixations are a fast and natural input method for VR/MR interaction. Previously, eye tracking in mobile VR suffered from low accuracy, long processing time, and the need for hardware add-ons such as anti-reflective lens coating and infrared emitters. We present an innovative mobile VR eye tracking methodology utilizing only the eye images from the front-facing (selfie) camera through the headset’s lens, without any modifications. Our system first enhances the low-contrast, poorly lit eye images by applying a pipeline of customised low-level image enhancements suppressing obtrusive lens reflections. We then propose an iris region-of-interest detection algorithm that is run only once. This increases the iris tracking speed by reducing the iris search space in mobile devices. We iteratively fit a customised geometric model to the iris to refine its coordinates. We display a thin bezel of light at the top edge of the screen for constant illumination. A confidence metric calculates the probability of successful iris detection. Calibration and linear gaze mapping between the estimated iris centroid and physical pixels on the screen results in low latency, real-time iris tracking. A formal study confirmed that our system’s accuracy is similar to eye trackers in commercial VR headsets in the central part of the headset’s field-of-view. In a VR game, gaze-driven user completion time was as fast as with head-tracked interaction, without the need for consecutive head motions. In a VR panorama viewer, users could successfully switch between panoramas using gaze.

Citation

Drakopoulos, P., Koulieris, G.-A., & Mania, K. (2021). Eye Tracking Interaction on Unmodified Mobile VR Headsets Using the Selfie Camera. ACM Transactions on Applied Perception, 18(3), 1-20. https://doi.org/10.1145/3456875

• Journal Article Type: Article
• Online Publication Date: May 20, 2021
• Publication Date: 2021-07
• Deposit Date: Sep 20, 2021
• Publicly Available Date: Nov 2, 2021
• Journal: ACM Transactions on Applied Perception
• Print ISSN: 1544-3558
• Electronic ISSN: 1544-3965
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 3
• Article Number: 11
• Pages: 1-20
• DOI: https://doi.org/10.1145/3456875
• Public URL: https://durham-repository.worktribe.com/output/1234106