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BayesGaze: A Bayesian Approach to Eye-Gaze Based Target Selection

Zhi Li 1 Maozheng Zhao 1 Yifan Wang 1 Sina Rashidian 1 Furqan Baig 1 Rui Liu 1 Wanyu Liu 2 Michel Beaudouin-Lafon 3 Brooke Ellison 1 Fusheng Wang 1 Xiaojun Bi 1 
3 EX-SITU - Extreme Situated Interaction
Inria Saclay - Ile de France, LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, IaH - Interaction avec l'Humain
Abstract : Selecting targets accurately and quickly with eye-gaze input remains an open research question. In this paper, we introduce BayesGaze, a Bayesian approach of determining the selected target given an eye-gaze trajectory. This approach views each sampling point in an eye-gaze trajectory as a signal for selecting a target. It then uses the Bayes' theorem to calculate the posterior probability of selecting a target given a sampling point, and accumulates the posterior probabilities weighted by sampling interval to determine the selected target. The selection results are fed back to update the prior distribution of targets, which is modeled by a categorical distribution. Our investigation shows that BayesGaze improves target selection accuracy and speed over a dwell-based selection method, and the Center of Gravity Mapping (CM) method. Our research shows that both accumulating posterior and incorporating the prior are effective in improving the performance of eye-gaze based target selection.
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Contributor : Michel Beaudouin-Lafon Connect in order to contact the contributor
Submitted on : Friday, July 16, 2021 - 2:47:34 PM
Last modification on : Friday, August 5, 2022 - 9:27:34 AM
Long-term archiving on: : Sunday, October 17, 2021 - 6:48:47 PM


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Zhi Li, Maozheng Zhao, Yifan Wang, Sina Rashidian, Furqan Baig, et al.. BayesGaze: A Bayesian Approach to Eye-Gaze Based Target Selection. GI 2021 - Graphics Interface, May 2021, Virtual event, Canada. pp.231-240, ⟨10.20380/GI2021.35⟩. ⟨hal-03288767⟩



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