Discriminant Patch Representation for RGB-D Face Recognition using Convolutional Neural Networks

Nesrine Grati, Achraf Ben-Hamadou, Mohamed Hammami

2019

Abstract

This paper focuses on designing data-driven models to learn a discriminant representation space for face recognition using RGB-D data. Unlike hand-crafted representations, learned models can extract and organize the discriminant information from the data, and can automatically adapt to build new compute vision applications faster. We proposed an effective way to train Convolutional Neural Networks to learn face patch discriminant features. The proposed solution was tested and validated on state-of-the-art RGB-D datasets and showed competitive and promising results relatively to standard hand-crafted feature extractors.

Paper Citation

in Harvard Style

Grati N., Ben-Hamadou A. and Hammami M. (2019). Discriminant Patch Representation for RGB-D Face Recognition using Convolutional Neural Networks. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 510-516. DOI: 10.5220/0007403305100516

in Bibtex Style

@conference{visapp19,
author={Nesrine Grati and Achraf Ben-Hamadou and Mohamed Hammami},
title={Discriminant Patch Representation for RGB-D Face Recognition using Convolutional Neural Networks},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={510-516},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007403305100516},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Discriminant Patch Representation for RGB-D Face Recognition using Convolutional Neural Networks
SN - 978-989-758-354-4
AU - Grati N.
AU - Ben-Hamadou A.
AU - Hammami M.
PY - 2019
SP - 510
EP - 516
DO - 10.5220/0007403305100516
PB - SciTePress