Sequential Forward Feature Selection for Facial Expression Recognition

Abstract

Facial expression recognition is an important computer vision problem with various applications. In this study, we investigate the effectiveness of features derived from facial landmarks in facial expression recognition. Distances between two combinations of facial landmarks constitute a distance vector. Features we use are the changes in the distance vectors extracted from expressive and neutral states of the face. The obtained feature vector contains elements that are relatively useless in expression recognition. By applying forward sequential feature selection, a subset of the most effective elements is formed. The chosen features are classified using a multi-class support vector machine. The performance of the proposed method is measured using Extended Cohn-Kanade dataset with seven expressions (anger, contempt, disgust, fear, happy, sad and surprised) and resulted in 89.9% mean class recognition accuracy.