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3D face reconstruction with region based best fit blending using mobile phone for virtual reality based social media

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journal contribution
posted on 10.06.2019, 08:57 authored by Gholamreza Anbarjafari, R.E. Haamer, I. Lusi, T. Tikk, L. Valgma
The use of virtual reality (VR) has been exponentially increasing and due to that many researchers have started to work on developing new VR based social media. For this purpose it is important to have an avatar of the user which look like them to be easily generated by the devices which are accessible, such as mobile phones. In this paper, we propose a novel method of recreating a 3D human face model captured with a phone camera image or video data. The method focuses more on model shape than texture in order to make the face recognizable. We detect 68 facial feature points and use them to separate a face into four regions. For each area the best fitting models are found and are further morphed combined to find the best fitting models for each area. These are then combined and further morphed in order to restore the original facial proportions. We also present a method of texturing the resulting model, where the aforementioned feature points are used to generate a texture for the resulting model.

Funding

This work has been partially supported by Estonian Research Council Grants (PUT638), The Scientific and Technological Research Council of Turkey (TÜBİTAK) (Proje 1001‒116E097), the Estonian Centre of Excellence in IT (EXCITE) funded by the European Regional Development Fund and the European Network on Integrating Vision and Language (iV&L Net) ICT COST Action IC1307.

History

School

  • Loughborough University London

Published in

The Bulletin of the Polish Academy of Sciences: Technical Sciences

Volume

67

Pages

125 - 132

Citation

ANBARJAFARI, G. ... et al., 2019. 3D face reconstruction with region based best fit blending using mobile phone for virtual reality based social media. The Bulletin of the Polish Academy of Sciences: Technical Sciences, 67(1), pp. 125 - 132.

Publisher

© the Authors. Published by the Polish Academy of Sciences

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2019

Notes

This is an Open Access Article. It is published by Polish Academy of Sciences under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

ISSN

2300-1917;0239-7528

Language

en