Chuan-Yu Chang, Jeng-Shiun Tsai, Chi-Jane Wang, P. Chung
{"title":"考虑面部表情和生理信号的情绪识别","authors":"Chuan-Yu Chang, Jeng-Shiun Tsai, Chi-Jane Wang, P. Chung","doi":"10.1109/CIBCB.2009.4925739","DOIUrl":null,"url":null,"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.","PeriodicalId":162052,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":"{\"title\":\"Emotion recognition with consideration of facial expression and physiological signals\",\"authors\":\"Chuan-Yu Chang, Jeng-Shiun Tsai, Chi-Jane Wang, P. Chung\",\"doi\":\"10.1109/CIBCB.2009.4925739\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":162052,\"journal\":{\"name\":\"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"50\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIBCB.2009.4925739\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIBCB.2009.4925739","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Emotion recognition with consideration of facial expression and physiological signals
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.