Gaze estimation problem tackled through synthetic images
| dc.contributor.author | Garde Lecumberri, Gonzalo | |
| dc.contributor.author | Larumbe Bergera, Andoni | |
| dc.contributor.author | Bossavit, Benoît | |
| dc.contributor.author | Cabeza Laguna, Rafael | |
| dc.contributor.author | Porta Cuéllar, Sonia | |
| 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 | 2021-03-05T11:32:50Z | |
| dc.date.available | 2021-03-05T11:32:50Z | |
| dc.date.issued | 2020 | |
| dc.description | Trabajo presentado al Symposium on Eye Tracking Research and Applications (ETRA ’20 Short Papers). Stuttgart, 2020 | es_ES |
| dc.description.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. | en |
| dc.description.sponsorship | The authors would like to acknowledge the Spanish Ministry of Science, Innovation and Universities for their support under Contract TIN2017-84388-R. | en |
| dc.format.extent | 5 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.1145/3379156.3391368 | |
| dc.identifier.isbn | 978-1-4503-7134-6 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/39351 | |
| dc.language.iso | eng | en |
| dc.publisher | Association for Computing Machinery (ACM) | en |
| dc.relation.ispartof | ETRA'20 Short Papers: ACM Symposium on Eye Tracking Research and Applications, 2020:16 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/ES/1PE/TIN2016-84388-R/ | |
| dc.relation.publisherversion | https://doi.org/10.1145/3379156.3391368 | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Neural networks | en |
| dc.subject | Datasets gaze estimation | en |
| dc.title | Gaze estimation problem tackled through synthetic images | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | e764f502-1d84-436f-81cd-97bfbe0240f4 | |
| relation.isAuthorOfPublication | 7d67c732-213a-47e0-82f8-81a897144cfa | |
| relation.isAuthorOfPublication | 45f0a653-c7ac-4322-80c3-735e9988330b | |
| relation.isAuthorOfPublication | 42fe20f8-5341-4c0e-8686-333ce816adfd | |
| relation.isAuthorOfPublication | 8f4eb99d-97ce-4dc9-b13a-18fcd2ab44e6 | |
| relation.isAuthorOfPublication | d3bfd5bf-8426-455b-bcc4-897ddb0d4c2e | |
| relation.isAuthorOfPublication.latestForDiscovery | e764f502-1d84-436f-81cd-97bfbe0240f4 |