Evolutional normal maps: 3D face representations for 2D-3D face recognition, face modelling and data augmentation
- 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.
Author of HS Reutlingen | Danner, Michael; Weber, Thomas; Rätsch, Matthias |
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URN: | urn:nbn:de:bsz:rt2-opus4-41773 |
DOI: | https://doi.org/10.5220/0010912000003124 |
ISBN: | 978-989-758-555-5 |
ISSN: | 2184-4321 |
Erschienen in: | Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) : 6-8 February, virtual |
Publisher: | SciTePress |
Place of publication: | Setúbal |
Document Type: | Conference proceeding |
Language: | English |
Publication year: | 2022 |
Tag: | 2D/3D face recognition; deep learning; local describers; machine vision; normal-vector-map representation; pattern recognition; visual understanding |
Volume: | 5: VISAPP |
Page Number: | 8 |
First Page: | 267 |
Last Page: | 274 |
DDC classes: | 620 Ingenieurwissenschaften und Maschinenbau |
Open access?: | Ja |
Licence (German): | Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |