A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme

In recent years, the importance of catching humans’ emotions grows larger as the artificial intelligence (AI) field is being developed. Facial expression recognition (FER) is a part of understanding the emotion of humans through facial expressions. We proposed a robust multi-depth network that can e...

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Autores principales:	Seo-Jeon Park, Byung-Gyu Kim, Naveen Chilamkurti
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning facial expression recognition (FER) 3D convolutional neural network (3D CNN) multirate signal processing minimum overlapped frame structure self-attention Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/bc0925915de2437394d506d1270f936b
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id	oai:doaj.org-article:bc0925915de2437394d506d1270f936b
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spelling	oai:doaj.org-article:bc0925915de2437394d506d1270f936b2021-11-11T19:00:10ZA Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme10.3390/s212169541424-8220https://doaj.org/article/bc0925915de2437394d506d1270f936b2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6954https://doaj.org/toc/1424-8220In recent years, the importance of catching humans’ emotions grows larger as the artificial intelligence (AI) field is being developed. Facial expression recognition (FER) is a part of understanding the emotion of humans through facial expressions. We proposed a robust multi-depth network that can efficiently classify the facial expression through feeding various and reinforced features. We designed the inputs for the multi-depth network as minimum overlapped frames so as to provide more spatio-temporal information to the designed multi-depth network. To utilize a structure of a multi-depth network, a multirate-based 3D convolutional neural network (CNN) based on a multirate signal processing scheme was suggested. In addition, we made the input images to be normalized adaptively based on the intensity of the given image and reinforced the output features from all depth networks by the self-attention module. Then, we concatenated the reinforced features and classified the expression by a joint fusion classifier. Through the proposed algorithm, for the CK+ database, the result of the proposed scheme showed a comparable accuracy of 96.23%. For the MMI and the GEMEP-FERA databases, it outperformed other state-of-the-art models with accuracies of 96.69% and 99.79%. For the AFEW database, which is known as one in a very wild environment, the proposed algorithm achieved an accuracy of 31.02%.Seo-Jeon ParkByung-Gyu KimNaveen ChilamkurtiMDPI AGarticledeep learningfacial expression recognition (FER)3D convolutional neural network (3D CNN)multirate signal processingminimum overlapped frame structureself-attentionChemical technologyTP1-1185ENSensors, Vol 21, Iss 6954, p 6954 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning facial expression recognition (FER) 3D convolutional neural network (3D CNN) multirate signal processing minimum overlapped frame structure self-attention Chemical technology TP1-1185
spellingShingle	deep learning facial expression recognition (FER) 3D convolutional neural network (3D CNN) multirate signal processing minimum overlapped frame structure self-attention Chemical technology TP1-1185 Seo-Jeon Park Byung-Gyu Kim Naveen Chilamkurti A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme
description	In recent years, the importance of catching humans’ emotions grows larger as the artificial intelligence (AI) field is being developed. Facial expression recognition (FER) is a part of understanding the emotion of humans through facial expressions. We proposed a robust multi-depth network that can efficiently classify the facial expression through feeding various and reinforced features. We designed the inputs for the multi-depth network as minimum overlapped frames so as to provide more spatio-temporal information to the designed multi-depth network. To utilize a structure of a multi-depth network, a multirate-based 3D convolutional neural network (CNN) based on a multirate signal processing scheme was suggested. In addition, we made the input images to be normalized adaptively based on the intensity of the given image and reinforced the output features from all depth networks by the self-attention module. Then, we concatenated the reinforced features and classified the expression by a joint fusion classifier. Through the proposed algorithm, for the CK+ database, the result of the proposed scheme showed a comparable accuracy of 96.23%. For the MMI and the GEMEP-FERA databases, it outperformed other state-of-the-art models with accuracies of 96.69% and 99.79%. For the AFEW database, which is known as one in a very wild environment, the proposed algorithm achieved an accuracy of 31.02%.
format	article
author	Seo-Jeon Park Byung-Gyu Kim Naveen Chilamkurti
author_facet	Seo-Jeon Park Byung-Gyu Kim Naveen Chilamkurti
author_sort	Seo-Jeon Park
title	A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme
title_short	A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme
title_full	A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme
title_fullStr	A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme
title_full_unstemmed	A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme
title_sort	robust facial expression recognition algorithm based on multi-rate feature fusion scheme
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bc0925915de2437394d506d1270f936b
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A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme

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