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Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a data...

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Autores principales: Minaee, Shervin, Minaei, Mehdi, Abdolrashidi, Amirali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123912/
https://www.ncbi.nlm.nih.gov/pubmed/33925371
http://dx.doi.org/10.3390/s21093046
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author Minaee, Shervin
Minaei, Mehdi
Abdolrashidi, Amirali
author_facet Minaee, Shervin
Minaei, Mehdi
Abdolrashidi, Amirali
author_sort Minaee, Shervin
collection PubMed
description Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.
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spelling pubmed-81239122021-05-16 Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network Minaee, Shervin Minaei, Mehdi Abdolrashidi, Amirali Sensors (Basel) Article Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face. MDPI 2021-04-27 /pmc/articles/PMC8123912/ /pubmed/33925371 http://dx.doi.org/10.3390/s21093046 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Minaee, Shervin
Minaei, Mehdi
Abdolrashidi, Amirali
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
title Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
title_full Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
title_fullStr Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
title_full_unstemmed Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
title_short Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
title_sort deep-emotion: facial expression recognition using attentional convolutional network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123912/
https://www.ncbi.nlm.nih.gov/pubmed/33925371
http://dx.doi.org/10.3390/s21093046
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