Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks
| dc.contributor.author | Larumbe Bergera, Andoni | |
| dc.contributor.author | Garde Lecumberri, Gonzalo | |
| 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.contributor.funder | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa | es |
| dc.date.accessioned | 2021-12-09T12:25:17Z | |
| dc.date.available | 2021-12-09T12:25:17Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance. | en |
| dc.description.sponsorship | This research was funded by Public University of Navarra (Pre-doctoral research grant) and by the Spanish Ministry of Science and Innovation under Contract 'Challenges of Eye Tracking Off-the-Shelf (ChETOS)' with reference: PID2020-118014RB-I00 | en |
| dc.format.extent | 14 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.3390/s21206847 | |
| dc.identifier.issn | 1424-8220 (Electronic) | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41212 | |
| dc.language.iso | eng | en |
| dc.publisher | MDPI | en |
| dc.relation.ispartof | Sensors 2021, 21, 6847 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118014RB-I00/ES/ | |
| dc.relation.publisherversion | https://doi.org/10.3390/s21206847 | |
| 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 | Eye tracking | en |
| dc.subject | Pupil center detection | en |
| dc.subject | Convolutional neural networks | en |
| dc.title | Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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