Physiological emotion analysis using support vector regression

• Published in: Neurocomputing (Amsterdam) Vol. 122; pp. 79 - 87
• Main Authors: Chang, Chuan-Yu, Chang, Chuan-Wang, Zheng, Jun-Ying, Chung, Pau-Choo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 25.12.2013
• Subjects: Emotion induction experiment; Emotion recognition; Emotion trend curve; Emotions; Face recognition; Facial; Physiological responses; Physiological signal; Recognition; Regression analysis; Support vector machines; Support vector regression; Trends; Emotion recognition; Support vector regression; Emotion trend curve; Emotion induction experiment; Physiological signal
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2013.02.041

Physical and mental diseases were deeply affected by stress and negative emotions. In general, emotions can be roughly recognized by facial expressions. Since facial expressions may be controlled and expressed differently by different people subjectively, inaccurate are very likely to happen. It is hard to control physiological responses and the corresponding signals while emotions are excited. Hence, an emotion recognition method that considers physiological signals is proposed in this paper. We designed a specific emotion induction experiment to collect five physiological signals of subjects including electrocardiogram, galvanic skin responses (GSR), blood volume pulse, and pulse. We use support vector regression (SVR) to train the trend curves of three emotions (sadness, fear, and pleasure). Experimental results show that the proposed method achieves high recognition rate up to 89.2%.