Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks
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 contributi...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/41212 |
| Acceso en línea: | https://hdl.handle.net/2454/41212 |
| Access Level: | acceso abierto |
| Palabra clave: | Eye tracking Pupil center detection Convolutional neural networks |
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Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networksLarumbe Bergera, AndoniGarde Lecumberri, GonzaloPorta Cuéllar, SoniaCabeza Laguna, RafaelVillanueva Larre, ArantxaEye trackingPupil center detectionConvolutional neural networksRemote 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.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-I00MDPIIngeniería Eléctrica, Electrónica y de ComunicaciónIngeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio IngeniaritzarenUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/41212reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118014RB-I00© 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.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/412122026-06-17T12:41:47Z |
| dc.title.none.fl_str_mv |
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks |
| title |
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks |
| spellingShingle |
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks Larumbe Bergera, Andoni Eye tracking Pupil center detection Convolutional neural networks |
| title_short |
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks |
| title_full |
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks |
| title_fullStr |
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks |
| title_full_unstemmed |
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks |
| title_sort |
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks |
| dc.creator.none.fl_str_mv |
Larumbe Bergera, Andoni Garde Lecumberri, Gonzalo Porta Cuéllar, Sonia Cabeza Laguna, Rafael Villanueva Larre, Arantxa |
| author |
Larumbe Bergera, Andoni |
| author_facet |
Larumbe Bergera, Andoni Garde Lecumberri, Gonzalo Porta Cuéllar, Sonia Cabeza Laguna, Rafael Villanueva Larre, Arantxa |
| author_role |
author |
| author2 |
Garde Lecumberri, Gonzalo Porta Cuéllar, Sonia Cabeza Laguna, Rafael Villanueva Larre, Arantxa |
| author2_role |
author author author author |
| dc.contributor.none.fl_str_mv |
Ingeniería Eléctrica, Electrónica y de Comunicación Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa |
| dc.subject.none.fl_str_mv |
Eye tracking Pupil center detection Convolutional neural networks |
| topic |
Eye tracking Pupil center detection Convolutional neural networks |
| description |
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. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://hdl.handle.net/2454/41212 |
| url |
https://hdl.handle.net/2454/41212 |
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Inglés |
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Inglés |
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118014RB-I00 |
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https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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MDPI |
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MDPI |
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reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra instname:Universidad Pública de Navarra |
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Universidad Pública de Navarra |
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