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

Kone Chaka,Cecile Belleudy,Remi Flamary,Nhan Le Thanh

doi:10.4172/2090-4886.1000153

Kone Chaka, Cecile Belleudy + Show 2 more

Open Access

https://doi.org/10.4172/2090-4886.1000153

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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.

Highlights

Communication specialists agree that more than 70% of communications are non-verbal
We propose to compare the performances of 2 classification algorithms in terms of their accuracy in the emotion recognition and their rapidity
After the implementation of the latter, we have developed and implemented a new algorithm called support vector machine (SVM) which, on the classification systems literature, performs better than the K nearest neighbors (KNN) cite

Summary

Introduction

Communication specialists agree that more than 70% of communications are non-verbal. Nonverbal communications rely on behaviors, gestures, facial expressions, and the intensity of a person’s voice. As well as non-verbal communication, emotions enable us to communicate with our environment. It is from this idea and the evolution of new technologies benefiting people’s health that affective computing was born. Affective computing is the study of interactions between technology and emotion to give machines the ability to understand, to interpret our emotions or even to express emotions. Affective computing offers many advantages such as the battle against depression, interactive games, E-Learning, etc

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