Publication: Improved strategies for HPE employing learning-by-synthesis approaches
| dc.contributor.author | Larumbe Bergera, Andoni | |
| dc.contributor.author | Ariz Galilea, Mikel | |
| dc.contributor.author | Bengoechea Irañeta, José Javier | |
| dc.contributor.author | Segura, Rubén | es_ES |
| 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 | 2021-12-09T12:25:18Z | |
| dc.date.available | 2021-12-09T12:25:18Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | The first contribution of this paper is the presentation of a synthetic video database where the groundtruth of 2D facial landmarks and 3D head poses is available to be used for training and evaluating Head Pose Estimation (HPE) methods. The database is publicly available and contains videos of users performing guided and natural movements. The second and main contribution is the submission of a hybrid method for HPE based on Pose from Ortography and Scaling by Iterations (POSIT). The 2D landmark detection is performed using Random Cascaded-Regression Copse (R-CR-C). For the training stage we use, state of the art labeled databases. Learning-by-synthesis approach has been also used to augment the size of the database employing the synthetic database. HPE accuracy is tested by using two literature 3D head models. The tracking method proposed has been compared with state of the art methods using Supervised Descent Regressors (SDR) in terms of accuracy, achieving an improvement of 60%. | en |
| dc.description.sponsorship | Spanish Ministry of Economy, Industry and Competitiveness, contract TIN2014-52897-R. | en |
| dc.format.extent | 10 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | A. Larumbe, M. Ariz, J. J. Bengoechea, R. Segura, R. Cabeza and A. Villanueva, 'Improved Strategies for HPE Employing Learning-by-Synthesis Approaches,' 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 1545-1554, doi: 10.1109/ICCVW.2017.182. | en |
| dc.identifier.doi | 10.1109/ICCVW.2017.182 | |
| dc.identifier.issn | 2473-9944 (Electronic) | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41213 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 1545-1554 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TIN2014-52897-R/ES/ | en |
| dc.relation.publisherversion | https://doi.org/10.1109/ICCVW.2017.182 | |
| dc.rights | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
| dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
| dc.subject | Databases | en |
| dc.subject | Three-dimensional displays | en |
| dc.subject | Face | en |
| dc.subject | Two dimensional displays | en |
| dc.subject | Solid modeling | en |
| dc.subject | Cameras | en |
| dc.title | Improved strategies for HPE employing learning-by-synthesis approaches | en |
| dc.type | info:eu-repo/semantics/conferenceObject | en |
| dc.type | Contribución a congreso / Biltzarrerako ekarpena | es |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | en |
| dc.type.version | Versión aceptada / Onetsi den bertsioa | es |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 7d67c732-213a-47e0-82f8-81a897144cfa | |
| relation.isAuthorOfPublication | 9d2b9c4c-9ede-4367-9ad9-8987addfbff6 | |
| relation.isAuthorOfPublication | e4686d1d-7687-4f70-81b2-2487764cabd7 | |
| relation.isAuthorOfPublication | 42fe20f8-5341-4c0e-8686-333ce816adfd | |
| relation.isAuthorOfPublication | d3bfd5bf-8426-455b-bcc4-897ddb0d4c2e | |
| relation.isAuthorOfPublication.latestForDiscovery | 7d67c732-213a-47e0-82f8-81a897144cfa |