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FERGCN: facial expression recognition based on graph convolution network

Due to the problems of occlusion, pose change, illumination change, and image blur in the wild facial expression dataset, it is a challenging computer vision problem to recognize facial expressions in a complex environment. To solve this problem, this paper proposes a deep neural network called faci...

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Detalles Bibliográficos
Autores principales: Liao, Lei, Zhu, Yu, Zheng, Bingbing, Jiang, Xiaoben, Lin, Jiajun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939244/
https://www.ncbi.nlm.nih.gov/pubmed/35342228
http://dx.doi.org/10.1007/s00138-022-01288-9
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author Liao, Lei
Zhu, Yu
Zheng, Bingbing
Jiang, Xiaoben
Lin, Jiajun
author_facet Liao, Lei
Zhu, Yu
Zheng, Bingbing
Jiang, Xiaoben
Lin, Jiajun
author_sort Liao, Lei
collection PubMed
description Due to the problems of occlusion, pose change, illumination change, and image blur in the wild facial expression dataset, it is a challenging computer vision problem to recognize facial expressions in a complex environment. To solve this problem, this paper proposes a deep neural network called facial expression recognition based on graph convolution network (FERGCN), which can effectively extract expression information from the face in a complex environment. The proposed FERGCN includes three essential parts. First, a feature extraction module is designed to obtain the global feature vectors from convolutional neural networks branch with triplet attention and the local feature vectors from key point-guided attention branch. Then, the proposed graph convolutional network uses the correlation between global features and local features to enhance the expression information of the non-occluded part, based on the topology graph of key points. Furthermore, the graph-matching module uses the similarity between images to enhance the network’s ability to distinguish different expressions. Results on public datasets show that our FERGCN can effectively recognize facial expressions in real environment, with RAF-DB of 88.23%, SFEW of 56.15% and AffectNet of 62.03%.
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spelling pubmed-89392442022-03-23 FERGCN: facial expression recognition based on graph convolution network Liao, Lei Zhu, Yu Zheng, Bingbing Jiang, Xiaoben Lin, Jiajun Mach Vis Appl Original Paper Due to the problems of occlusion, pose change, illumination change, and image blur in the wild facial expression dataset, it is a challenging computer vision problem to recognize facial expressions in a complex environment. To solve this problem, this paper proposes a deep neural network called facial expression recognition based on graph convolution network (FERGCN), which can effectively extract expression information from the face in a complex environment. The proposed FERGCN includes three essential parts. First, a feature extraction module is designed to obtain the global feature vectors from convolutional neural networks branch with triplet attention and the local feature vectors from key point-guided attention branch. Then, the proposed graph convolutional network uses the correlation between global features and local features to enhance the expression information of the non-occluded part, based on the topology graph of key points. Furthermore, the graph-matching module uses the similarity between images to enhance the network’s ability to distinguish different expressions. Results on public datasets show that our FERGCN can effectively recognize facial expressions in real environment, with RAF-DB of 88.23%, SFEW of 56.15% and AffectNet of 62.03%. Springer Berlin Heidelberg 2022-03-22 2022 /pmc/articles/PMC8939244/ /pubmed/35342228 http://dx.doi.org/10.1007/s00138-022-01288-9 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Liao, Lei
Zhu, Yu
Zheng, Bingbing
Jiang, Xiaoben
Lin, Jiajun
FERGCN: facial expression recognition based on graph convolution network
title FERGCN: facial expression recognition based on graph convolution network
title_full FERGCN: facial expression recognition based on graph convolution network
title_fullStr FERGCN: facial expression recognition based on graph convolution network
title_full_unstemmed FERGCN: facial expression recognition based on graph convolution network
title_short FERGCN: facial expression recognition based on graph convolution network
title_sort fergcn: facial expression recognition based on graph convolution network
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939244/
https://www.ncbi.nlm.nih.gov/pubmed/35342228
http://dx.doi.org/10.1007/s00138-022-01288-9
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