Abstract
An emotion recognition system with consideration of facial expression and physiological signals is proposed in this paper. A specific designed mood induction experiment is performed to collect facial expressing images and physiological signals of subjects. We detected 14 feature points and extracted 12 facial features from facial expression images. Meanwhile, we measure the skin conductivity, finger temperature and heart rate from the subject. Both facial and physiological features are adopted to train the classifiers. Two learning vector quantization (LVQ) neural networks were applied to classify four emotions: love, joy, surprise and fear. Experimental results show the proposed recognition system is able to identify four emotions by facial expressions, physiological signals, and both of them.
| Original language | English |
|---|---|
| Title of host publication | 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings |
| Pages | 278-283 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 Jul 20 |
| Event | 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Nashville, TN, United States Duration: 2009 Mar 30 → 2009 Apr 2 |
Publication series
| Name | 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings |
|---|
Other
| Other | 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 |
|---|---|
| Country | United States |
| City | Nashville, TN |
| Period | 09-03-30 → 09-04-02 |
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All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Artificial Intelligence
- Computational Theory and Mathematics
Cite this
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Emotion recognition with consideration of facial expression and physiological signals. / Chang, Chuan Yu; Tsai, Jeng Shiun; Wang, Chi-Jen; Chung, Pau-Choo.
2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings. 2009. p. 278-283 4925739 (2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Emotion recognition with consideration of facial expression and physiological signals
AU - Chang, Chuan Yu
AU - Tsai, Jeng Shiun
AU - Wang, Chi-Jen
AU - Chung, Pau-Choo
PY - 2009/7/20
Y1 - 2009/7/20
N2 - An emotion recognition system with consideration of facial expression and physiological signals is proposed in this paper. A specific designed mood induction experiment is performed to collect facial expressing images and physiological signals of subjects. We detected 14 feature points and extracted 12 facial features from facial expression images. Meanwhile, we measure the skin conductivity, finger temperature and heart rate from the subject. Both facial and physiological features are adopted to train the classifiers. Two learning vector quantization (LVQ) neural networks were applied to classify four emotions: love, joy, surprise and fear. Experimental results show the proposed recognition system is able to identify four emotions by facial expressions, physiological signals, and both of them.
AB - An emotion recognition system with consideration of facial expression and physiological signals is proposed in this paper. A specific designed mood induction experiment is performed to collect facial expressing images and physiological signals of subjects. We detected 14 feature points and extracted 12 facial features from facial expression images. Meanwhile, we measure the skin conductivity, finger temperature and heart rate from the subject. Both facial and physiological features are adopted to train the classifiers. Two learning vector quantization (LVQ) neural networks were applied to classify four emotions: love, joy, surprise and fear. Experimental results show the proposed recognition system is able to identify four emotions by facial expressions, physiological signals, and both of them.
UR - http://www.scopus.com/inward/record.url?scp=67650348192&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67650348192&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2009.4925739
DO - 10.1109/CIBCB.2009.4925739
M3 - Conference contribution
AN - SCOPUS:67650348192
SN - 9781424427567
T3 - 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings
SP - 278
EP - 283
BT - 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings
ER -