Analysis of physiological for emotion recognition with the IRS model

• Published in: Neurocomputing (Amsterdam) Vol. 178; pp. 103 - 111
• Main Authors: Li, Chao, Xu, Chao, Feng, Zhiyong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 20.02.2016
• Subjects: Affective computing; Emotion recognition; Emotions; Face recognition; Facial; Facial expression; Galvanic skin response; Human behavior; Individual response specificity; Pattern recognition; Physiology-based; Recognition; User-independent system; Individual response specificity; Affective computing; Emotion recognition; Physiology-based; User-independent system; Facial expression
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2015.07.112

Facial expression-based emotion recognition has attracted lots of attention. Higher accurate performance could be expected with help of the other cues, e.g. physiological signals, for some specific blue such as ‘Poker Face’ and the lack of facial expression. In this paper, physiological signals are utilized for inferring user׳s emotion. However, the results of physiological-based emotion recognition are still inaccurate in the user-independent scenario since most existing methods ignore the difference in individual response pattern. To this end, we propose a Group-Based IRS (Individual Response Specificity) model to improve performance of physiological-based emotion recognition by taking user׳s IRS into account. The main contributions of this paper are two-fold: (1) an affective physiological database is collected to analyze human׳s emotional response pattern. The physiological signals are recorded from 30 subjects in four induced emotions (neutral, sadness, fear and pleasure). Three-channel bio-sensors are used to measure users electrocardiogram (ECG), galvanic skin response (GSR) and photo plethysmography (PPG). (2) In the experiment, the Group-based IRS model is proposed for emotion recognition in user-independent scenario, the effectiveness of which has been validated on our database. The results show that the Group-based IRS model can achieve higher recognition precision than the general model. •We collect affective physiological dataset under four induced emotions.•The theory of IRS is adopted to build a recognition model.•We analyze and discuss the robustness and applicability of physiological signals.•The Group-Based IRS model is utilized to improve performance of emotion recognition.