Facial recognition and 3D non-rigid registration

Title	Facial recognition and 3D non-rigid registration
Publication Type	Conference Paper
Year of Publication	2020
Authors	Makovetskii, A., Kober, V., Voronin, A., Zhernov, D.
Conference Name	2020 International Conference on Information Technology and Nanotechnology (ITNT)
Date Published	May 2020
Publisher	IEEE
ISBN Number	978-1-7281-7041-1
Keywords	3D nonrigid registration, 3D point clouds, canonical face representation, convolutional neural network, convolutional neural network (CNN), convolutional neural networks, face recognition, facial expressions, facial recognition, facial recognition performance, Human Behavior, human face recognition, human faces, image registration, iterative closest points (ICP), Iterative methods, Metrics, Nanotechnology, neural nets, Neural networks, non-rigid ICP, nonrigid Iterative Closest Point algorithm, point clouds, pubcrawl, resampling method, resilience, Resiliency, Three-dimensional displays, Tools
Abstract	One of the most efficient tool for human face recognition is neural networks. However, the result of recognition can be spoiled by facial expressions and other deviation from the canonical face representation. In this paper, we propose a resampling method of human faces represented by 3D point clouds. The method is based on a non-rigid Iterative Closest Point (ICP) algorithm. To improve the facial recognition performance, we use a combination of the proposed method and convolutional neural network (CNN). Computer simulation results are provided to illustrate the performance of the proposed method.
URL	https://ieeexplore.ieee.org/document/9253224
DOI	10.1109/ITNT49337.2020.9253224
Citation Key	makovetskii_facial_2020