Physiological Responses-Based Emotion Recognition Using Multi-Class SVM with RBF Kernel

Abstract

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

Switching and optimizing control for coal flotation process based on a hybrid model

“…The selection of the kernel function is critical in the process of LS-SVM modeling. The RBF has been frequently applied in classification due to its favorable learning ability, excellent nonlinear mapping performance and simple form [ 31 , 32 ]. Therefore, the RBF is selected as the kernel function of the LS-SVM.…”

Study on A Fault Diagnosis Method of Rolling Element Bearing Based on Improved ACO and SVM Model

“…In this paper, the SVM model is used to classify the complex data. Due to the RBF with good learning ability, simple form, symmetry radial, good smoothness and analyticity, the RBF is widely applied to classify the data with the low dimension, high dimension, small sample, and large sample [27][28][29]. The RBF is shown as follow:…”