Article (Scientific journals)
Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Recognition
Papadopoulos, Konstantinos; KACEM, Anis; Shabayek, Abdelrahman et al.
2022In 2022 8th International Conference on Virtual Reality
Peer reviewed
 

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Abstract :
[en] Face 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 in the performance of such a method. In this paper, we propose a novel framework for dynamic 3D face 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 of face videos, a Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we evaluate our approach on a challenging dynamic 3D facial expression dataset.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Computer Vision Imaging & Machine Intelligence (CVI²)
Disciplines :
Computer science
Author, co-author :
Papadopoulos, Konstantinos;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
KACEM, Anis ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Shabayek, Abdelrahman;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
AOUADA, Djamila  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
External co-authors :
no
Language :
English
Title :
Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Recognition
Publication date :
28 May 2022
Journal title :
2022 8th International Conference on Virtual Reality
ISSN :
2331-9542
eISSN :
2331-9569
Publisher :
IEEE, Nanjing, China
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
FnR Project :
FNR11643091 - Face Identification Under Deformations, 2017 (01/05/2018-31/10/2021) - Djamila Aouada
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since 05 November 2021

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