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Low-Cost Eye Tracking Calibration: A Knowledge-Based Study †
Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-bas...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347006/ https://www.ncbi.nlm.nih.gov/pubmed/34372344 http://dx.doi.org/10.3390/s21155109 |
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author | Garde, Gonzalo Larumbe-Bergera, Andoni Bossavit, Benoît Porta, Sonia Cabeza, Rafael Villanueva, Arantxa |
author_facet | Garde, Gonzalo Larumbe-Bergera, Andoni Bossavit, Benoît Porta, Sonia Cabeza, Rafael Villanueva, Arantxa |
author_sort | Garde, Gonzalo |
collection | PubMed |
description | Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems. |
format | Online Article Text |
id | pubmed-8347006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83470062021-08-08 Low-Cost Eye Tracking Calibration: A Knowledge-Based Study † Garde, Gonzalo Larumbe-Bergera, Andoni Bossavit, Benoît Porta, Sonia Cabeza, Rafael Villanueva, Arantxa Sensors (Basel) Article Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems. MDPI 2021-07-28 /pmc/articles/PMC8347006/ /pubmed/34372344 http://dx.doi.org/10.3390/s21155109 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Garde, Gonzalo Larumbe-Bergera, Andoni Bossavit, Benoît Porta, Sonia Cabeza, Rafael Villanueva, Arantxa Low-Cost Eye Tracking Calibration: A Knowledge-Based Study † |
title | Low-Cost Eye Tracking Calibration: A Knowledge-Based Study † |
title_full | Low-Cost Eye Tracking Calibration: A Knowledge-Based Study † |
title_fullStr | Low-Cost Eye Tracking Calibration: A Knowledge-Based Study † |
title_full_unstemmed | Low-Cost Eye Tracking Calibration: A Knowledge-Based Study † |
title_short | Low-Cost Eye Tracking Calibration: A Knowledge-Based Study † |
title_sort | low-cost eye tracking calibration: a knowledge-based study † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347006/ https://www.ncbi.nlm.nih.gov/pubmed/34372344 http://dx.doi.org/10.3390/s21155109 |
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