Authors:
Michael Danner
1
;
2
;
Patrik Huber
1
;
3
;
Muhammad Awais
1
;
Zhen-Hua Feng
1
;
Josef Kittler
1
and
Matthias Raetsch
2
Affiliations:
1
Centre for Vision, Speech & Signal Processing, University of Surrey, Guildford, U.K.
;
2
ViSiR, Reutlingen University, Reutlingen, Germany
;
3
Department of Computer Science, University of York, York, U.K.
Keyword(s):
Face Recognition, Deep Learning, 3D Morphable Face Model, 3D Reconstruction.
Abstract:
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 preco
nditions but also outperforms standard 2D methods from recent years.
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