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Texture-based 3D Face Recognition using Deep Neural Networks for unconstrained Human-Machine Interaction

Danner, Michael, Raetsch, Matthias, Huber, Patrik, Awais, Muhammad, Feng, Zhenhua and Kittler, Josef (2019) Texture-based 3D Face Recognition using Deep Neural Networks for unconstrained Human-Machine Interaction In: The International Conference on Computer Vision Theory and Applications (VISAPP) 2020, 2020-02-27-2020-02-29, Valletta, Malta.

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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 preconditions but also outperforms standard 2D methods from recent years.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Raetsch, Matthias
Date : 3 December 2019
Copyright Disclaimer : Copyright 2020 The Author(s)
Uncontrolled Keywords : Face recognition; Deep Learning; 3D Morphable Face Model; 3D Reconstruction
Related URLs :
Depositing User : Diane Maxfield
Date Deposited : 22 Jan 2020 12:15
Last Modified : 27 Feb 2020 02:08

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