Performance Comparison of the KNN and SVM Classification Algorithms in the Emotion Detection System EMOTICA

Kone Chaka, Nhan Le-Thanh,

Abstract

Emotica (EMOTIon CApture) system is a multimodal emotion recognition system that uses physiological signals. A DLF (Decision Level Fusion) approach with a voting method is used in this system to merge monomodal decisions for a multimodal detection. In this document, on the one hand, we describe how from a physiological signal Emotica can detect an emotional activity and distinguish one emotional activity from others. On the other hand, we present a study about two classification algorithms, KNN and SVM. These algorithms have been implemented on the Emotica system in order to see which one is the best. The experiments show that KNN and SVM allow a high accuracy in emotion recognition, but SVM is more accurate than KNN on the data that was used. Indeed, we obtain a recognition rate of 81.69% and 84% respectively with KNN and SVM algorithms under certain conditions.