ScreenGlint
Michael Xuelin Huang, Jiajia Li, Grace Ngai, Hong Va Leong · 2017
Gaze estimation has widespread applications. However, little work has explored gaze estimation on smartphones, even though they are fast becoming ubiquitous. This paper presents ScreenGlint, a novel approach which exploits the glint (reflection) of the screen on the user's cornea for gaze estimation, using only the image captured by the front-facing camera. We first conduct a user study on common postures during smartphone use. We then design an experiment to evaluate the accuracy of ScreenGlint under varying face-to-screen distances. An in-depth evaluation involving multiple users is conducted and the impact of head pose variations is investigated. ScreenGlint achieves an overall angular error of 2.44º without head pose variations, and 2.94º with head pose variations. Our technique compares favorably to state-of-the-art research works, indicating that the glint of the screen is an effective and practical cue to gaze estimation on the smartphone platform. We believe that this work can open up new possibilities for practical and ubiquitous gaze-aware applications.