Particle Swarm Optimization Algorithm for Facial Image Expression Classification

Image mining is used to mine knowledge from large image databases. Image segmentation, image compression, image clustering, image classification and image retrieval are significant image mining tasks. Face detection methods are used to identify the similar faces from the large collection of facial images. It has numerous computer vision applications and it has many research challenges such as rotation, scale, pose and illumination variation. Facial expression is defined as the position of the muscles beneath the skin of the face and it is a form of nonverbal communication. Facial expressions are the expression which shows the emotions and different feelings of human beings. Different facial expressions are sad, happy, fear, normal, surprise and angry. In this research work facial expressions are classified by using the optimization algorithms. PSO with LIBSVM algorithm is proposed for facial expression classification and the performance of this algorithm is compared with the existing BAT algorithm. The results of the existing and proposed algorithms are analyzed based on the two performance factors; they are classification accuracy and execution time. From the experimental results, we observed that the proposed PSO with LIBSVM algorithm has produced good results compared to existing BAT algorithm. This work is implemented in MATLAB 7.0.