Synthetic gaze data augmentation for improved user calibration
| dc.contributor.author | Garde Lecumberri, Gonzalo | |
| dc.contributor.author | Larumbe Bergera, Andoni | |
| dc.contributor.author | Porta Cuéllar, Sonia | |
| dc.contributor.author | Cabeza Laguna, Rafael | |
| dc.contributor.author | Villanueva Larre, Arantxa | |
| dc.contributor.department | Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.department | Ingeniería Eléctrica, Electrónica y de Comunicación | es_ES |
| dc.date.accessioned | 2021-12-09T12:25:17Z | |
| dc.date.available | 2022-02-21T00:00:15Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | In this paper, we focus on the calibration possibilitiesó of a deep learning based gaze estimation process applying transfer learning, comparing its performance when using a general dataset versus when using a gaze specific dataset in the pretrained model. Subject calibration has demonstrated to improve gaze accuracy in high performance eye trackers. Hence, we wonder about the potential of a deep learning gaze estimation model for subject calibration employing fine-tuning procedures. A pretrained Resnet-18 network, which has great performance in many computer vision tasks, is fine-tuned using user’s specific data in a few shot adaptive gaze estimation approach. We study the impact of pretraining a model with a synthetic dataset, U2Eyes, before addressing the gaze estimation calibration in a real dataset, I2Head. The results of the work show that the success of the individual calibration largely depends on the balance between fine-tuning and the standard supervised learning procedures and that using a gaze specific dataset to pretrain the model improves the accuracy when few images are available for calibration. This paper shows that calibration is feasible in low resolution scenarios providing outstanding accuracies below 1.5 ∘ ∘ of error. | en |
| dc.embargo.lift | 2022-02-21 | |
| dc.embargo.terms | 2022-02-21 | |
| dc.format.extent | 13 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Garde G., Larumbe-Bergera A., Porta S., Cabeza R., Villanueva A. (2021) Synthetic Gaze Data Augmentation for Improved User Calibration. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_27 | en |
| dc.identifier.doi | 10.1007/978-3-030-68796-0_27 | |
| dc.identifier.isbn | 978-3-030-68796-0 (Electronic) | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41211 | |
| dc.language.iso | eng | en |
| dc.publisher | Springer | en |
| dc.relation.ispartof | Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12663. Springer, Cham. 978-3-030-68796-0 | en |
| dc.relation.publisherversion | https://doi.org/10.1007/978-3-030-68796-0_27 | |
| dc.rights | © Springer Nature Switzerland AG 2021 | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Gaze estimation | en |
| dc.subject | Calibration | en |
| dc.subject | Transfer learning | en |
| dc.title | Synthetic gaze data augmentation for improved user calibration | en |
| dc.type | info:eu-repo/semantics/bookPart | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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