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Authors: Walid Hariri 1 ; Hedi Tabia 2 ; Nadir Farah 3 ; David Declercq 2 and Abdallah Benouareth 3

Affiliations: 1 University of Cergy-Pontoise, CNRS, UMR 8051 and Badji Mokhtar Annaba University, France ; 2 University of Cergy-Pontoise, CNRS and UMR 8051, France ; 3 Badji Mokhtar Annaba University, Algeria

Keyword(s): LBP, HoS, Bag-of-Features, Codebook, Depth Image, Term Vector.

Abstract: In this paper, we present an efficient method for 3D face recognition based on vector quantization of both geometrical and visual proprieties of the face. The method starts by describing each 3D face using a set of orderless features, and use then the Bag-of-Features paradigm to construct the face signature. We analyze the performance of three well-known classifiers: the Naïve Bayes, the Multilayer perceptron and the Random forests. The results reported on the FRGCv2 dataset show the effectiveness of our approach and prove that the method is robust to facial expression.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Hariri, W. ; Tabia, H. ; Farah, N. ; Declercq, D. and Benouareth, A. (2017). Geometrical and Visual Feature Quantization for 3D Face Recognition. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP; ISBN 978-989-758-226-4; ISSN 2184-4321, SciTePress, pages 187-193. DOI: 10.5220/0006101701870193

@conference{visapp17,
author={Walid Hariri and Hedi Tabia and Nadir Farah and David Declercq and Abdallah Benouareth},
title={Geometrical and Visual Feature Quantization for 3D Face Recognition},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP},
year={2017},
pages={187-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006101701870193},
isbn={978-989-758-226-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP
TI - Geometrical and Visual Feature Quantization for 3D Face Recognition
SN - 978-989-758-226-4
IS - 2184-4321
AU - Hariri, W.
AU - Tabia, H.
AU - Farah, N.
AU - Declercq, D.
AU - Benouareth, A.
PY - 2017
SP - 187
EP - 193
DO - 10.5220/0006101701870193
PB - SciTePress

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