TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Danner, Michael
A1  - Huber, Patrik
A1  - Awais, Muhammad
A1  - Feng, Zhen-Hua
A1  - Kittler, Josef
A1  - Rätsch, Matthias
ED  - Farinella, Giovanni
ED  - Radeva, Petia
ED  - Braz, Jose
T1  - Texture-based 3D face recognition using deep neural networks for unconstrained human-machine interaction
T2  - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 5), 27-29 February 2020, Valletta, Malta
N2  - 3D assisted 2D face recognition involves the process of reconstructing 3D faces from 2D images and solving the problem of face recognition in 3D. To facilitate the use of deep neural networks, a 3D face, normally represented as a 3D mesh of vertices and its corresponding surface texture, is remapped to image-like square isomaps by a conformal mapping. Based on previous work, we assume that face recognition benefits more from texture. In this work, we focus on the surface texture and its discriminatory information content for recognition purposes. Our approach is to prepare a 3D mesh, the corresponding surface texture and the original 2D image as triple input for the recognition network, to show that 3D data is useful for face recognition. Texture enhancement methods to control the texture fusion process are introduced and we adapt data augmentation methods. Our results show that texture-map-based face recognition can not only compete with state-of-the-art systems under the same precon ditions but also outperforms standard 2D methods from recent years.
KW  - face recognition
KW  - deep learning
KW  - 3D morphable face model
KW  - 3D reconstruction
Y1  - 2020
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-30613
SN  - 2184-4321
SS  - 2184-4321
SN  - 978-989-758-402-2
SB  - 978-989-758-402-2
U6  - https://doi.org/10.5220/0008982504200427
DO  - https://doi.org/10.5220/0008982504200427
SP  - 420
EP  - 427
S1  - 8
PB  - SCITEPRESS
CY  - Setúbal, Portugal
ER  -