Emotion Classification Using Ensemble of Convolutional Neural Networks and Support Vector Machine

This paper presents an ensemble of convolutional neural networks (CNNs) and support vector machine (SVM) for classifying emotions from electroencephalogram (EEG) patterns. We used popular deep learning models for feature extraction and a support vector machine classifier is employed to classify the EEG patterns into suitable emotion classes. The main contribution of this work is to investigate on the following points: creating an ensemble of pre-trained deep learning networks with support vector machine classifier (SVM) for classifying emotional states of person for single and multiple emotional attributes. Finding out the best ensemble network, extracting suitable layer and robust features to improve the classification accuracy of support vector machine and finally to compare the performance of ensemble of networks with stand-alone deep learning networks. Two popular convolutional neural networks are used for experiments: Alex Net and GoogLeNet. All experiments are carried out on database for emotion analysis using physiological signals (DEAP). A thorough analysis of experimental results revealed that classification accuracy of 87.5% is achieved by ensemble of Alex Net and SVM for single attribute (valance) classification while for two attributes (arousal and valance) the accuracy achieved is 62.5%. Similarly, accuracy of 100% and 62.5% are achieved for single and two attributes classification respectively using ensemble of GoogLeNet and SVM.