Geometrical and Visual Feature Quantization for 3D Face Recognition

Walid Hariri, Hedi Tabia, Nadir Farah, David Declercq, Abdallah Benouareth

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.

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Paper Citation


in Harvard Style

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 - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 187-193. DOI: 10.5220/0006101701870193


in Bibtex Style

@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 - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={187-193},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006101701870193},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Geometrical and Visual Feature Quantization for 3D Face Recognition
SN - 978-989-758-226-4
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