Head pose estimation based on 3-D facial landmarks localization and regression

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• dc.contributor.author Derkach, Dmytroca
• dc.contributor.author Ruiz Ovejero, Adriàca
• dc.contributor.author Sukno, Federico Mateoca
• dc.date.accessioned 2018-02-13T11:50:58Z
• dc.date.available 2018-02-13T11:50:58Z
• dc.date.issued 2017
• dc.description Comunicació presentada a la 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), celebrada els dies 30 de maig a 3 de juny de 2017 a Washington DC, EUA.
• dc.description.abstract In this paper we present a system that is able to estimate head pose using only depth information from consumer RGB-D cameras such as Kinect 2. In contrast to most approaches addressing this problem, we do not rely on tracking and produce pose estimation in terms of pitch, yaw and roll angles using single depth frames as input. Our system combines three different methods for pose estimation: two of them are based on state-of-the-art landmark detection and the third one is a dictionarybased approach that is able to work in especially challenging scans where landmarks or mesh correspondences are too difficult to obtain. We evaluated our system on the SASE database, which consists of ~30K frames from 50 subjects. We obtained average pose estimation errors between 5 and 8 degrees per angle, achieving the best performance in the FG2017 Head Pose Estimation Challenge. Full code of the developed system is available on-line.en
• dc.description.sponsorship This work is partly supported by the Spanish Ministry of Economy and Competitiveness under the Ramon y Cajal fellowships and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
• dc.format.mimetype application/pdf
• dc.identifier.citation Derkach D, Ruiz A, Sukno FM. Head pose estimation based on 3-D facial landmarks localization and regression. In: 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017); 2017 May 30-June 3; Washington DC, WA. Piscataway (NJ); IEEE; 2017. p. 820-7. DOI: 10.1109/FG.2017.104
• dc.identifier.doi http://dx.doi.org/10.1109/FG.2017.104
• dc.identifier.uri http://hdl.handle.net/10230/33845
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)ca
• dc.relation.ispartof 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017); 2017 May 30-June 3; Washington DC, WA. Piscataway (NJ); IEEE; 2017. p. 820-7.
• 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 works. The final published article can be found at http://ieeexplore.ieee.org/document/7961827/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Pose estimationen
• dc.subject.keyword Three-dimensional displaysen
• dc.subject.keyword Faceen
• dc.subject.keyword Noseen
• dc.subject.keyword Feature extractionen
• dc.title Head pose estimation based on 3-D facial landmarks localization and regressionca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion