Emotion recognition for human-machine communication

Author

Maaoui, Choubeila ; PRUSKI, Alain ; ABDAT, Faiza

Author_Institution

Lab. d´´Autom. des Syst. Cooperatifs, Univ. de Metz, Metz

fYear

2008

fDate

22-26 Sept. 2008

Firstpage

1210

Lastpage

1215

Abstract

The ability to recognize emotion is one of the hallmarks of emotion intelligence. This paper proposed to recognize emotion using physiological signals obtained from multiple subjects. IAPS images were used to elicit target emotions. Five physiological signals: Blood volume pulse (BVP), Electromyography (EMG), Skin Conductance (SC), Skin Temperature (SKT) and Respiration (RESP) were selected to extract 30 features for recognition. Two pattern classification methods, Fisher discriminant and SVM method are used and compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 92% over six emotional states.

Keywords

electromyography; emotion recognition; human computer interaction; pattern classification; physiology; support vector machines; Fisher discriminant; SVM; blood volume pulse; electromyography; emotion intelligence; emotion recognition; human-machine communication; pattern classification; physiological signals; respiration; skin conductance; skin temperature; Classification algorithms; Emotion recognition; Feature extraction; Manganese; Sensors; Skin; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on

Conference_Location

Nice

Print_ISBN

978-1-4244-2057-5

Type

conf

DOI

10.1109/IROS.2008.4650870

Filename

4650870