Reference : Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition
E-prints/Working papers : Already available on another site
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/48519
Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition
English
Papadopoulos, Konstantinos mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Kacem, Anis mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Shabayek, Abdelrahman mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
20-Apr-2021
No
[en] Face identification/recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions. This has two disadvantages. First, important facial shape cues are ignored. Second, facial deformations due to expressions can have an impact on the performance of such a method. In this paper, we propose a novel framework for dynamic 3D face identification/recognition based on facial keypoints. Each dynamic sequence of facial expressions is represented as a spatio-temporal graph, which is constructed using 3D facial landmarks. Each graph node contains local shape and texture features that are extracted from its neighborhood. For the classification/identification of faces, a Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we evaluate our approach on a challenging dynamic 3D facial expression dataset.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Computer Vision Imaging & Machine Intelligence (CVI┬▓)
http://hdl.handle.net/10993/48519
https://arxiv.org/abs/2104.09145
FnR ; FNR11643091 > Djamila Aouada > IDform > Face Identification Under Deformations > 01/05/2018 > 31/10/2021 > 2017

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