DRER: Deep Learning–Based Driver’s Real Emotion Recognizer

In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while dri...
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Autor*in:	Geesung Oh [verfasserIn] Junghwan Ryu [verfasserIn] Euiseok Jeong [verfasserIn] Ji Hyun Yang [verfasserIn] Sungwook Hwang [verfasserIn] Sangho Lee [verfasserIn] Sejoon Lim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	human–machine interface emotion recognition real emotion driver’s emotional state deep learning sensor fusion

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 6, p 2166
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:6, p 2166

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21062166

Katalog-ID:	DOAJ084322837

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520			\|a In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation.
650		4	\|a human–machine interface
650		4	\|a emotion recognition
650		4	\|a real emotion
650		4	\|a driver’s emotional state
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650		4	\|a sensor fusion
653		0	\|a Chemical technology
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spelling	10.3390/s21062166 doi (DE-627)DOAJ084322837 (DE-599)DOAJ95800ceaab8c404b87cb02018e9897d7 DE-627 ger DE-627 rakwb eng TP1-1185 Geesung Oh verfasserin aut DRER: Deep Learning–Based Driver’s Real Emotion Recognizer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation. human–machine interface emotion recognition real emotion driver’s emotional state deep learning sensor fusion Chemical technology Junghwan Ryu verfasserin aut Euiseok Jeong verfasserin aut Ji Hyun Yang verfasserin aut Sungwook Hwang verfasserin aut Sangho Lee verfasserin aut Sejoon Lim verfasserin aut In Sensors MDPI AG, 2003 21(2021), 6, p 2166 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:6, p 2166 https://doi.org/10.3390/s21062166 kostenfrei https://doaj.org/article/95800ceaab8c404b87cb02018e9897d7 kostenfrei https://www.mdpi.com/1424-8220/21/6/2166 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 6, p 2166
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allfieldsGer	10.3390/s21062166 doi (DE-627)DOAJ084322837 (DE-599)DOAJ95800ceaab8c404b87cb02018e9897d7 DE-627 ger DE-627 rakwb eng TP1-1185 Geesung Oh verfasserin aut DRER: Deep Learning–Based Driver’s Real Emotion Recognizer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation. human–machine interface emotion recognition real emotion driver’s emotional state deep learning sensor fusion Chemical technology Junghwan Ryu verfasserin aut Euiseok Jeong verfasserin aut Ji Hyun Yang verfasserin aut Sungwook Hwang verfasserin aut Sangho Lee verfasserin aut Sejoon Lim verfasserin aut In Sensors MDPI AG, 2003 21(2021), 6, p 2166 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:6, p 2166 https://doi.org/10.3390/s21062166 kostenfrei https://doaj.org/article/95800ceaab8c404b87cb02018e9897d7 kostenfrei https://www.mdpi.com/1424-8220/21/6/2166 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 6, p 2166
allfieldsSound	10.3390/s21062166 doi (DE-627)DOAJ084322837 (DE-599)DOAJ95800ceaab8c404b87cb02018e9897d7 DE-627 ger DE-627 rakwb eng TP1-1185 Geesung Oh verfasserin aut DRER: Deep Learning–Based Driver’s Real Emotion Recognizer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation. human–machine interface emotion recognition real emotion driver’s emotional state deep learning sensor fusion Chemical technology Junghwan Ryu verfasserin aut Euiseok Jeong verfasserin aut Ji Hyun Yang verfasserin aut Sungwook Hwang verfasserin aut Sangho Lee verfasserin aut Sejoon Lim verfasserin aut In Sensors MDPI AG, 2003 21(2021), 6, p 2166 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:6, p 2166 https://doi.org/10.3390/s21062166 kostenfrei https://doaj.org/article/95800ceaab8c404b87cb02018e9897d7 kostenfrei https://www.mdpi.com/1424-8220/21/6/2166 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 6, p 2166
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source	In Sensors 21(2021), 6, p 2166 volume:21 year:2021 number:6, p 2166
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callnumber-first	T - Technology
author	Geesung Oh
spellingShingle	Geesung Oh misc TP1-1185 misc human–machine interface misc emotion recognition misc real emotion misc driver’s emotional state misc deep learning misc sensor fusion misc Chemical technology DRER: Deep Learning–Based Driver’s Real Emotion Recognizer
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topic_title	TP1-1185 DRER: Deep Learning–Based Driver’s Real Emotion Recognizer human–machine interface emotion recognition real emotion driver’s emotional state deep learning sensor fusion
topic	misc TP1-1185 misc human–machine interface misc emotion recognition misc real emotion misc driver’s emotional state misc deep learning misc sensor fusion misc Chemical technology
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title	DRER: Deep Learning–Based Driver’s Real Emotion Recognizer
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title_sort	drer: deep learning–based driver’s real emotion recognizer
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title_auth	DRER: Deep Learning–Based Driver’s Real Emotion Recognizer
abstract	In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation.
abstractGer	In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation.
abstract_unstemmed	In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation.
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title_short	DRER: Deep Learning–Based Driver’s Real Emotion Recognizer
url	https://doi.org/10.3390/s21062166 https://doaj.org/article/95800ceaab8c404b87cb02018e9897d7 https://www.mdpi.com/1424-8220/21/6/2166 https://doaj.org/toc/1424-8220
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up_date	2024-07-03T22:24:26.234Z
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Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. 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