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Texture-based 3D face recognition using deep neural networks for unconstrained human-machine interaction

  • 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.

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Author of HS ReutlingenDanner, Michael; Rätsch, Matthias
Erschienen in: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
Place of publication:Setúbal, Portugal
Editor:Giovanni Farinella, Petia Radeva, Jose Braz
Document Type:Conference proceeding
Publication year:2020
Tag:3D morphable face model; 3D reconstruction; deep learning; face recognition
Page Number:8
First Page:420
Last Page:427
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International