Facial Expression Recognition Using Facial Landmarks and Random Forest Classifier

Human emotions are the universally common mode of interaction. Automated human facial expression identification has its own advantages. In this paper, the author has proposed and developed a methodology to identify facial emotions using facial landmarks and random forest classifier. Firstly, faces are identified in each image using a histogram of oriented gradients with a linear classifier, image pyramid, and sliding window detection scheme. Then facial landmarks are identified using a model trained with the iBUG 300-W dataset. A feature vector is calculated using a proposed method which uses identified facial landmarks and it is normalized using a proposed method in order to remove facial size variations. The same feature vector is calculated for the neutral pose and vector difference is used to identify emotions using random forest classifier. Famous Extended Cohn-Kanade database has been used to train random forest and to test the accuracy of the system.