Fully end-to-end composite recurrent convolution network for deformable facial tracking in the wild

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• dc.contributor.author Sukno, Federico Mateo
• dc.contributor.author Aspandi, Decky
• dc.contributor.author Martínez, Oriol
• dc.contributor.author Sukno, Federico Mateo
• dc.contributor.author Binefa i Valls, Xavier
• dc.date.accessioned 2020-11-16T12:59:46Z
• dc.date.available 2020-11-16T12:59:46Z
• dc.date.issued 2019
• dc.description Comunicació presentada a: Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition celebrat del 14 al 18 de maig a Lille, França.
• dc.description.abstract Human facial tracking is an important task in computer vision, which has recently lost pace compared to other facial analysis tasks. The majority of current available tracker possess two major limitations: their little use of temporal information and the widespread use of handcrafted features, without taking full advantage of the large annotated datasets that have recently become available. In this paper we present a fully end-to-end facial tracking model based on current state of the art deep model architectures that can be effectively trained from the available annotated facial landmark datasets. We build our model from the recently introduced general object tracker Re 3 , which allows modeling the short and long temporal dependency between frames by means of its internal Long Short Term Memory (LSTM) layers. Facial tracking experiments on the challenging 300-VW dataset show that our model can produce state of the art accuracy and far lower failure rates than competing approaches. We specifically compare the performance of our approach modified to work in tracking-by-detection mode and showed that, as such, it can produce results that are comparable to state of the art trackers. However, upon activation of our tracking mechanism, the results improve significantly, confirming the advantage of taking into account temporal dependencies.
• dc.description.sponsorship This work is partly supported by the Spanish Ministry of Economy and Competitiveness under project grant TIN2017- 90124-P, the Ramon y Cajal programme, and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
• dc.format.mimetype application/pdf
• dc.identifier.citation Aspandi D, Martinez O, Sukno F, Binefa X. Fully end-to-end composite recurrent convolution network for deformable facial tracking in the wild. In: Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition; 2019 May 14-18; Lille, France. [New York]: IEEE; 2019. p. 115-22. DOI: 10.1109/FG.2019.8756630
• dc.identifier.doi http://dx.doi.org/10.1109/FG.2019.8756630
• dc.identifier.isbn 978-1-7281-0089-0
• dc.identifier.uri http://hdl.handle.net/10230/45781
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition; 2019 May 14-18; Lille, France. [New York]: IEEE; 2019.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/TIN2017-90124-P
• dc.rights © 2019 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. http://dx.doi.org/10.1109/FG.2019.8756630
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.title Fully end-to-end composite recurrent convolution network for deformable facial tracking in the wild
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion