Evolutional Normal Maps: 3D Face Representations for 2D-3D Face Recognition, Face Modelling and Data Augmentation

Michael Danner, Michael Danner, Thomas Weber, Patrik Huber, Patrik Huber, Muhammad Awais, Matthias Raetsch, Josef Kittler

2022

Abstract

We address the problem of 3D face recognition based on either 3D sensor data, or on a 3D face reconstructed from a 2D face image. We focus on 3D shape representation in terms of a mesh of surface normal vectors. The first contribution of this work is an evaluation of eight different 3D face representations and their multiple combinations. An important contribution of the study is the proposed implementation, which allows these representations to be computed directly from 3D meshes, instead of point clouds. This enhances their computational efficiency. Motivated by the results of the comparative evaluation, we propose a 3D face shape descriptor, named Evolutional Normal Maps, that assimilates and optimises a subset of six of these approaches. The proposed shape descriptor can be modified and tuned to suit different tasks. It is used as input for a deep convolutional network for 3D face recognition. An extensive experimental evaluation using the Bosphorus 3D Face, CASIA 3D Face and JNU-3D Face datasets shows that, compared to the state of the art methods, the proposed approach is better in terms of both computational cost and recognition accuracy.

Paper Citation

in Harvard Style

Danner M., Weber T., Huber P., Awais M., Raetsch M. and Kittler J. (2022). Evolutional Normal Maps: 3D Face Representations for 2D-3D Face Recognition, Face Modelling and Data Augmentation. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 267-274. DOI: 10.5220/0010912000003124

in Bibtex Style

@conference{visapp22,
author={Michael Danner and Thomas Weber and Patrik Huber and Muhammad Awais and Matthias Raetsch and Josef Kittler},
title={Evolutional Normal Maps: 3D Face Representations for 2D-3D Face Recognition, Face Modelling and Data Augmentation},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={267-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010912000003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Evolutional Normal Maps: 3D Face Representations for 2D-3D Face Recognition, Face Modelling and Data Augmentation
SN - 978-989-758-555-5
AU - Danner M.
AU - Weber T.
AU - Huber P.
AU - Awais M.
AU - Raetsch M.
AU - Kittler J.
PY - 2022
SP - 267
EP - 274
DO - 10.5220/0010912000003124
PB - SciTePress