Facial Expression Emotion through BCI-based Personal Traits and Emotion Classification

Abstract

In this paper, we propose a system that can classify personal propensity and recognize emotional information by using the user's biometric information, EEG. In addition, the facial expression generation module according to individual dispositions was proposed by mapping the emotional information to the facial expression. Using the differences in facial expressions according to individual propensities classified in this way, mapping is performed from the El Fuzzy Model to the size of facial expressions according to traits. Emotion recognition uses the absolute value of the differential coefficient of EEG data as a feature value and classifies it using the Support Vector Machine (SVM). After classifying each disposition and emotion, facial emotion information is generated based on the classified information. The emotional information classification system based on brainwave data proposed in this paper is expected to be helpful in the study of human-computer interaction (HCI) in the era of the 4th industrial revolution by intelligently classifying facial expressions according to user's emotions.