Facial expression recognition using iterative universum twin support vector machine

Facial expressions are one of the most important characteristics of human behaviour. They are very useful in applications on human computer interaction. To classify facial emotions, different feature extraction methods are used with machine learning techniques. In supervised learning, information about the distribution of data is given by data points not belonging to any of the classes. These data points are known as universum data. In this work, we use universum data to perform multiclass classification of facial emotions from human facial images. Moreover, the existing universum based models suffer from the drawback of high training cost, so we propose an iterative universum twin support vector machine (IUTWSVM) using Newton method. Our IUTWSVM gives good generalization performance with less computation cost. To solve the optimization problem of proposed IUTWSVM, no optimization toolbox is required. Further, improper selection of universum points always leads to degraded performance of the model. For generating better universum, a novel scheme is proposed in this work based on information entropy of data. To check the effectiveness of proposed IUTWSVM, several numerical experiments are performed on benchmark real world datasets. For multiclass classification of facial emotions, the performance of IUTWSVM is compared with existing algorithms using different feature extraction techniques. Our proposed algorithm shows better generalization performance with less training cost in both binary as well as multiclass classification problems.