Deep learning based facial expression recognition using improved Cat Swarm Optimization

Abstract

Human emotional facial expressions play a vital role in interpersonal relations. Automated facial expression recognition has always remained a challenging problem in real-life applications as people vary significantly in the way of showing their expressions. Recently various approaches have been proposed for automatically analyzing the facial expression of a person. In this paper, a novel approach to human facial expression recognition by applying a modified version of the Cat Swarm Optimization (CSO) algorithm, called Improved Cat Swarm Optimization (ICSO) algorithm is proposed. The input image given to the proposed system retrieves similar images from the dataset as well as identifies the person’s emotional state through facial expressions. Deep features present in the face image are extracted using Deep Convolution Neural Network (DCNN) approach. ICSO is proposed to select optimal features from the face image that can uniquely distinguish the facial expression of a person. Employing DCNN with ICSO improves the retrieval performance of the proposed system. Ensemble classifiers that employ Neural Network (NN) and Support Vector Machine (SVM) are implemented to classify facial expressions such as normal, happy, sad, surprise, fear and angry. The performance of the proposed system is evaluated using JAFFE, CK+, Pie datasets and some real-world images. The proposed system outperforms the existing system, thus achieving superior accuracy and reduced computation time.