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dc.creatorAriz Galilea, Mikeles_ES
dc.creatorVillanueva Larre, Arantxaes_ES
dc.creatorCabeza Laguna, Rafaeles_ES
dc.date.accessioned2019-06-24T11:13:21Z
dc.date.available2019-06-24T11:13:21Z
dc.date.issued2019
dc.identifier.issn1077-3142
dc.identifier.urihttps://hdl.handle.net/2454/33480
dc.description.abstractHead pose estimation (HPE) is currently a growing research field, mainly because of the proliferation of human–computer interfaces (HCI) in the last decade. It offers a wide variety of applications, including human behavior analysis, driver assistance systems or gaze estimation systems. This article aims to contribute to the development of robust and accurate HPE methods based on 2D tracking of the face, enhancing performance of both 2D point tracking and 3D pose estimation. We start with a baseline method for pose estimation based on POSIT algorithm. A novel weighted variant of POSIT is then proposed, together with a methodology to estimate weights for the 2D–3D point correspondences. Further, outlier detection and correction methods are also proposed in order to enhance both point tracking and pose estimation. With the aim of achieving a wider impact, the problem is addressed using a global approach: all the methods proposed are generalizable to any kind of object for which an approximate 3D model is available. These methods have been evaluated for the specific task of HPE using two different head pose video databases; a recently published one that reflects the expected performance of the system in current technological conditions, and an older one that allows an extensive comparison with state-of-the-art HPE methods. Results show that the proposed enhancements improve the accuracy of both 2D facial point tracking and 3D HPE, with respect to the implemented baseline method, by over 15% in normal tracking conditions and over 30% in noisy tracking conditions. Moreover, the proposed HPE system outperforms the state of the art on the two databases.en
dc.description.sponsorshipWe would like to acknowledge the Spanish Ministry of Economy, Industry and Competitiveness for their support under Grant “Formación de Profesorado Universitario AP2010-5191 ” and Contract “ TIN2014-52897-R ”.en
dc.format.extent10 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofComputer Vision And Image Understanding, Volume 180, March 2019, Pages 13-22en
dc.rights© 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFacial point detection and trackingen
dc.subjectHead trackingen
dc.subjectOutlier correctionen
dc.subjectPose estimationen
dc.subjectPOSITen
dc.titleRobust and accurate 2D-tracking-based 3D positioning method: application to head pose estimationen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Ingeniaritza Elektriko, Elektroniko eta Telekomunikazio Sailaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1016/j.cviu.2019.01.002
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TIN2014-52897
dc.relation.publisherversionhttps://doi.org/10.1016/j.cviu.2019.01.002
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


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© 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.
Except where otherwise noted, this item's license is described as © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.