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