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10.1145/3379156.3391368acmconferencesArticle/Chapter ViewAbstractPublication PagesetraConference Proceedingsconference-collections
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Gaze estimation problem tackled through synthetic images

Published: 02 June 2020 Publication History

Abstract

In this paper, we evaluate a synthetic framework to be used in the field of gaze estimation employing deep learning techniques. The lack of sufficient annotated data could be overcome by the utilization of a synthetic evaluation framework as far as it resembles the behavior of a real scenario. In this work, we use U2Eyes synthetic environment employing I2Head datataset as real benchmark for comparison based on alternative training and testing strategies. The results obtained show comparable average behavior between both frameworks although significantly more robust and stable performance is retrieved by the synthetic images. Additionally, the potential of synthetically pretrained models in order to be applied in user’s specific calibration strategies is shown with outstanding performances.

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  • (2021)Low-Cost Eye Tracking Calibration: A Knowledge-Based StudySensors10.3390/s2115510921:15(5109)Online publication date: 28-Jul-2021

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cover image ACM Conferences
ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications
June 2020
305 pages
ISBN:9781450371346
DOI:10.1145/3379156
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 02 June 2020

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  1. datasets gaze estimation
  2. neural networks

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  • (2021)Low-Cost Eye Tracking Calibration: A Knowledge-Based StudySensors10.3390/s2115510921:15(5109)Online publication date: 28-Jul-2021

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