A hybrid bioinspired algorithm for facial emotion recognition using CSO-GA-PSO-SVM

Abstract

Human-Computer Interaction gets more natural when the machine can detect human emotions faster and accurate. A lot of research is being carried out in the field of affective computing in order to improve the accuracy with speed. Bio-inspired algorithms for feature extraction and classification stages, has improved accuracy and speed further. In this paper, we propose a hybrid algorithm using CSO (Cat Swarm Optimization) with PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) for emotion recognition (ER). This bio inspired algorithm in conjunction with the support vector machine (SVM) will find an optimal feature set from a bigger set. Results from CK+ (Cohn Kanade) [1] dataset demonstrate that our proposed method using CSO-GA-PSOSVM outperforms Emotion Recognition System with CSOSVM by 10.5% in accuracy. This paper also proposes a new E-Learning [2] system to demonstrate its effectiveness and efficiency in real-time scenario. The proposed algorithm is applied over the facial characteristics captured from students in teaching-learning environment. The optimized feature vector obtained is passed to the SVM classifier for classification. Experimental results yield 99% classification accuracy in a person dependent mode with six basic emotions namely Happy, Sad, Anger, Disgust, Surprise and Neutral. � 2015 IEEE.