Application of support vector regression for phyciological emotion recognition

Chuan Yu Chang, Jun Ying Zheng, Chi-Jen Wang, Pau-Choo Chung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Cases of physical and mental diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response. Physiological responses and the corresponding signals are difficult to control when a person is overcome with emotion. Therefore, an emotion recognition system that considers physiological signals is proposed in this paper. An emotion induction experiment was performed to collect five physiological signals from subjects, namely electrocardiogram, respiration, galvanic skin response (GSR), blood volume pulse, and pulse. Support vector regression (SVR) was used to train three trend curves of three emotions (sadness, fear, and pleasure). Experimental results show that the proposed method has a high recognition rate of 90.6%.

Original languageEnglish
Title of host publicationICS 2010 - International Computer Symposium
Pages12-17
Number of pages6
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 International Computer Symposium, ICS 2010 - Tainan, Taiwan
Duration: 2010 Dec 162010 Dec 18

Publication series

NameICS 2010 - International Computer Symposium

Other

Other2010 International Computer Symposium, ICS 2010
CountryTaiwan
CityTainan
Period10-12-1610-12-18

Fingerprint

Electrocardiography
Skin
Blood
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Chang, C. Y., Zheng, J. Y., Wang, C-J., & Chung, P-C. (2010). Application of support vector regression for phyciological emotion recognition. In ICS 2010 - International Computer Symposium (pp. 12-17). [5685532] (ICS 2010 - International Computer Symposium). https://doi.org/10.1109/COMPSYM.2010.5685532
Chang, Chuan Yu ; Zheng, Jun Ying ; Wang, Chi-Jen ; Chung, Pau-Choo. / Application of support vector regression for phyciological emotion recognition. ICS 2010 - International Computer Symposium. 2010. pp. 12-17 (ICS 2010 - International Computer Symposium).
@inproceedings{87e51e608a0340e4aa23dd4c833a1841,
title = "Application of support vector regression for phyciological emotion recognition",
abstract = "Cases of physical and mental diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response. Physiological responses and the corresponding signals are difficult to control when a person is overcome with emotion. Therefore, an emotion recognition system that considers physiological signals is proposed in this paper. An emotion induction experiment was performed to collect five physiological signals from subjects, namely electrocardiogram, respiration, galvanic skin response (GSR), blood volume pulse, and pulse. Support vector regression (SVR) was used to train three trend curves of three emotions (sadness, fear, and pleasure). Experimental results show that the proposed method has a high recognition rate of 90.6{\%}.",
author = "Chang, {Chuan Yu} and Zheng, {Jun Ying} and Chi-Jen Wang and Pau-Choo Chung",
year = "2010",
month = "12",
day = "1",
doi = "10.1109/COMPSYM.2010.5685532",
language = "English",
isbn = "9781424476404",
series = "ICS 2010 - International Computer Symposium",
pages = "12--17",
booktitle = "ICS 2010 - International Computer Symposium",

}

Chang, CY, Zheng, JY, Wang, C-J & Chung, P-C 2010, Application of support vector regression for phyciological emotion recognition. in ICS 2010 - International Computer Symposium., 5685532, ICS 2010 - International Computer Symposium, pp. 12-17, 2010 International Computer Symposium, ICS 2010, Tainan, Taiwan, 10-12-16. https://doi.org/10.1109/COMPSYM.2010.5685532

Application of support vector regression for phyciological emotion recognition. / Chang, Chuan Yu; Zheng, Jun Ying; Wang, Chi-Jen; Chung, Pau-Choo.

ICS 2010 - International Computer Symposium. 2010. p. 12-17 5685532 (ICS 2010 - International Computer Symposium).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Application of support vector regression for phyciological emotion recognition

AU - Chang, Chuan Yu

AU - Zheng, Jun Ying

AU - Wang, Chi-Jen

AU - Chung, Pau-Choo

PY - 2010/12/1

Y1 - 2010/12/1

N2 - Cases of physical and mental diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response. Physiological responses and the corresponding signals are difficult to control when a person is overcome with emotion. Therefore, an emotion recognition system that considers physiological signals is proposed in this paper. An emotion induction experiment was performed to collect five physiological signals from subjects, namely electrocardiogram, respiration, galvanic skin response (GSR), blood volume pulse, and pulse. Support vector regression (SVR) was used to train three trend curves of three emotions (sadness, fear, and pleasure). Experimental results show that the proposed method has a high recognition rate of 90.6%.

AB - Cases of physical and mental diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response. Physiological responses and the corresponding signals are difficult to control when a person is overcome with emotion. Therefore, an emotion recognition system that considers physiological signals is proposed in this paper. An emotion induction experiment was performed to collect five physiological signals from subjects, namely electrocardiogram, respiration, galvanic skin response (GSR), blood volume pulse, and pulse. Support vector regression (SVR) was used to train three trend curves of three emotions (sadness, fear, and pleasure). Experimental results show that the proposed method has a high recognition rate of 90.6%.

UR - http://www.scopus.com/inward/record.url?scp=79851469190&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79851469190&partnerID=8YFLogxK

U2 - 10.1109/COMPSYM.2010.5685532

DO - 10.1109/COMPSYM.2010.5685532

M3 - Conference contribution

AN - SCOPUS:79851469190

SN - 9781424476404

T3 - ICS 2010 - International Computer Symposium

SP - 12

EP - 17

BT - ICS 2010 - International Computer Symposium

ER -

Chang CY, Zheng JY, Wang C-J, Chung P-C. Application of support vector regression for phyciological emotion recognition. In ICS 2010 - International Computer Symposium. 2010. p. 12-17. 5685532. (ICS 2010 - International Computer Symposium). https://doi.org/10.1109/COMPSYM.2010.5685532