Michael Danner

Michael Danner

Postgraduate Research Student
Diploma Information Science

Academic and research departments

Faculty of Engineering and Physical Sciences, Centre for Vision, Speech and Signal Processing (CVSSP).

Publications

Michael Danner, Matthias Raetsch, Patrik Huber, Muhammad Awais, Zhenhua Feng, Josef Kittler (2019)Texture-based 3D Face Recognition using Deep Neural Networks for unconstrained Human-Machine Interaction SCITEPRESS

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.

Michael Danner, Matthias Raetsch, Patrik Huber, Muhammad Awais, Zhenhua Feng, Josef Kittler (2020)Texture-based 3D Face Recognition using Deep Neural Networks for unconstrained Human-Machine Interaction, In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020 SCITEPRESS
DOI: 10.5220/0008982504200427

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.