Low-cost eye tracking calibration: a knowledge-based study
| dc.contributor.author | Garde Lecumberri, Gonzalo | |
| dc.contributor.author | Larumbe Bergera, Andoni | |
| dc.contributor.author | Bossavit, Benoît | |
| dc.contributor.author | Porta Cuéllar, Sonia | |
| dc.contributor.author | Cabeza Laguna, Rafael | |
| dc.contributor.author | Villanueva Larre, Arantxa | |
| dc.contributor.department | Ingeniería Eléctrica, Electrónica y de Comunicación | es_ES |
| dc.contributor.department | Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren | eu |
| dc.date.accessioned | 2021-12-09T12:25:16Z | |
| dc.date.available | 2021-12-09T12:25:16Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | 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 | en |
| dc.format.extent | 21 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.3390/s21155109 | |
| dc.identifier.issn | 1424-8220 (Electronic) | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41210 | |
| dc.language.iso | eng | en |
| dc.publisher | MDPI | en |
| dc.relation.ispartof | Sensors 2021, 21, 5109 | en |
| dc.relation.publisherversion | https://doi.org/10.3390/s21155109 | |
| dc.rights | © 2021 by the authors. 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. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Gaze-estimation | en |
| dc.subject | Calibration | en |
| dc.subject | Low-resolution | en |
| dc.subject | Theoretical analysis | en |
| dc.title | Low-cost eye tracking calibration: a knowledge-based study | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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