Emotion Recognition Based on Multi-Variant Correlation of Physiological Signals

Abstract

Emotion recognition based on affective physiological changes is a pattern recognition problem, and selecting specific physiological signals is necessary and helpful to recognize the emotions. Fingertip blood oxygen saturation (OXY), galvanic skin response (GSR) and heart rate (HR) are acquired while amusement, anger, grief and fear of 101 subjects are individually elicited by films. The affective physiological changes in multi-subject GSR, the first derivative of GSR (FD_GSR) and HR are detected by the multi-variant correlation method. The correlation analysis reveals that multi-subject HR, GSR and FD_GSR fluctuations respectively have common intra-class affective patterns. In addition to the conventional features of HR and GSR, the affective HR, GSR and FD_GSR fluctuations are quantified by the local scaling dimension and applied as the affective features. The multi-subject affective database containing 477 cases is classified by a Random Forests classifier. An overall correct rate of 74 percent for quinary classification of amusement, anger, grief, fear and the baseline state are obtained.