Three-D Wide Faces (3DWF): facial landmark detection and 3D reconstruction over a new RGB-D multi-camera dataset

Quintana González, Marcos and Karaoglu, Sezer and Álvarez Garcia, Federico and Menéndez García, José Manuel and Gevers, Theo (2019). Three-D Wide Faces (3DWF): facial landmark detection and 3D reconstruction over a new RGB-D multi-camera dataset. "Sensors", v. 19 (n. 1103); pp. 1-21. ISSN 1424-8220. https://doi.org/10.3390/s19051103.

Description

Title: Three-D Wide Faces (3DWF): facial landmark detection and 3D reconstruction over a new RGB-D multi-camera dataset
Author/s:
  • Quintana González, Marcos
  • Karaoglu, Sezer
  • Álvarez Garcia, Federico
  • Menéndez García, José Manuel
  • Gevers, Theo
Item Type: Article
Título de Revista/Publicación: Sensors
Date: March 2019
ISSN: 1424-8220
Volume: 19
Subjects:
Freetext Keywords: face landmark detection, 3D face modelling, head pose classification, 3D data collection, deep learning
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Latest advances of deep learning paradigm and 3D imaging systems have raised the necessity for more complete datasets that allow exploitation of facial features such as pose, gender or age. In our work, we propose a new facial dataset collected with an innovative RGB–D multi-camera setup whose optimization is presented and validated. 3DWF includes 3D raw and registered data collection for 92 persons from low-cost RGB–D sensing devices to commercial scanners with great accuracy. 3DWF provides a complete dataset with relevant and accurate visual information for different tasks related to facial properties such as face tracking or 3D face reconstruction by means of annotated density normalized 2K clouds and RGB–D streams. In addition, we validate the reliability of our proposal by an original data augmentation method from a massive set of face meshes for facial landmark detection in 2D domain, and by head pose classification through common Machine Learning techniques directed towards proving alignment of collected data.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020825619AI4EUTHALES SERVICES SASA European AI On Demand Platform and Ecosystem

More information

Item ID: 54494
DC Identifier: http://oa.upm.es/54494/
OAI Identifier: oai:oa.upm.es:54494
DOI: 10.3390/s19051103
Official URL: https://www.mdpi.com/journal/sensors
Deposited by: Memoria Investigacion
Deposited on: 01 Apr 2019 16:45
Last Modified: 01 Apr 2019 16:45
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