Publication | Closed Access
Remarks on emotion recognition from multi-modal bio-potential signals
45
Citations
7
References
2004
Year
Unknown Venue
Support Vector MachineEngineeringMachine LearningFacial Expression RecognitionPattern RecognitionBiometricsAffective NeuroscienceAffective ComputingNeuroimagingSocial SciencesNeuroscienceMultimodal Sentiment AnalysisSvm ClassifierEmotionEmotion RecognitionEmotional ResponseEmotion Recognition System
This paper proposes an emotion recognition system from multi-modal bio-potential signals. For emotion recognition, two types of classifier: neural network (NN) and support vector machine (SVM) are designed and investigated. Using gathered data under psychological emotion stimulation experiments, the classifiers are trained and tested. In experiments of recognizing two emotion: pleasure and unpleasure, recognition rates of 62.3% with the NN classifier and 59.7% with the SVM classifier are achieved. The experimental result shows that using multi-modal bio-potential signals is feasible and that NN and/or SVM are well suited for emotion recognition tasks.
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