Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs...

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Autores principales:	Taejae Jeon, Han Byeol Bae, Yongju Lee, Sungjun Jang, Sangyoun Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning stress recognition stress database spatial attention temporal attention facial landmark Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/fd4838c6636545f797504a666d47f8e5
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id	oai:doaj.org-article:fd4838c6636545f797504a666d47f8e5
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spelling	oai:doaj.org-article:fd4838c6636545f797504a666d47f8e52021-11-25T18:56:55ZDeep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information10.3390/s212274981424-8220https://doaj.org/article/fd4838c6636545f797504a666d47f8e52021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7498https://doaj.org/toc/1424-8220In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.Taejae JeonHan Byeol BaeYongju LeeSungjun JangSangyoun LeeMDPI AGarticledeep learningstress recognitionstress databasespatial attentiontemporal attentionfacial landmarkChemical technologyTP1-1185ENSensors, Vol 21, Iss 7498, p 7498 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning stress recognition stress database spatial attention temporal attention facial landmark Chemical technology TP1-1185
spellingShingle	deep learning stress recognition stress database spatial attention temporal attention facial landmark Chemical technology TP1-1185 Taejae Jeon Han Byeol Bae Yongju Lee Sungjun Jang Sangyoun Lee Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information
description	In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.
format	article
author	Taejae Jeon Han Byeol Bae Yongju Lee Sungjun Jang Sangyoun Lee
author_facet	Taejae Jeon Han Byeol Bae Yongju Lee Sungjun Jang Sangyoun Lee
author_sort	Taejae Jeon
title	Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information
title_short	Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information
title_full	Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information
title_fullStr	Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information
title_full_unstemmed	Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information
title_sort	deep-learning-based stress recognition with spatial-temporal facial information
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fd4838c6636545f797504a666d47f8e5
work_keys_str_mv	AT taejaejeon deeplearningbasedstressrecognitionwithspatialtemporalfacialinformation AT hanbyeolbae deeplearningbasedstressrecognitionwithspatialtemporalfacialinformation AT yongjulee deeplearningbasedstressrecognitionwithspatialtemporalfacialinformation AT sungjunjang deeplearningbasedstressrecognitionwithspatialtemporalfacialinformation AT sangyounlee deeplearningbasedstressrecognitionwithspatialtemporalfacialinformation
_version_	1718410565664112640

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

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