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Vision 2018, 2(3), 35;

Accurate Model-Based Point of Gaze Estimation on Mobile Devices

Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON M5T 3A9, Canada
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
Author to whom correspondence should be addressed.
Received: 1 July 2018 / Revised: 17 August 2018 / Accepted: 21 August 2018 / Published: 24 August 2018
(This article belongs to the Special Issue Development of Advanced Eye-tracking Technologies and Applications)
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The most accurate remote Point of Gaze (PoG) estimation methods that allow free head movements use infrared light sources and cameras together with gaze estimation models. Current gaze estimation models were developed for desktop eye-tracking systems and assume that the relative roll between the system and the subjects’ eyes (the ’R-Roll’) is roughly constant during use. This assumption is not true for hand-held mobile-device-based eye-tracking systems. We present an analysis that shows the accuracy of estimating the PoG on screens of hand-held mobile devices depends on the magnitude of the R-Roll angle and the angular offset between the visual and optical axes of the individual viewer. We also describe a new method to determine the PoG which compensates for the effects of R-Roll on the accuracy of the POG. Experimental results on a prototype infrared smartphone show that for an R-Roll angle of 90 ° , the new method achieves accuracy of approximately 1 ° , while a gaze estimation method that assumes that the R-Roll angle remains constant achieves an accuracy of 3.5 ° . The manner in which the experimental PoG estimation errors increase with the increase in the R-Roll angle was consistent with the analysis. The method presented in this paper can improve significantly the performance of eye-tracking systems on hand-held mobile-devices. View Full-Text
Keywords: Eye Tracking; Gaze Estimation; Mobile Computing; Mobile Eye-Tracking; Gaze-Based Interaction Eye Tracking; Gaze Estimation; Mobile Computing; Mobile Eye-Tracking; Gaze-Based Interaction

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Brousseau, B.; Rose, J.; Eizenman, M. Accurate Model-Based Point of Gaze Estimation on Mobile Devices. Vision 2018, 2, 35.

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