Facial expression recognition using geometric features

Recognition of emotions by analyzing the facial expressions has been an important research area in last decade. Existing studies in the literature mostly seek for a classification model that is linking the emotions with the facial expressions that are defined by the basic components of face (such as eyes, mouth, and ear). In this regard, Artificial Neural Networks (ANN) has been a widely employed approach for classification purposes. Being different than the existing approaches, we searched for the points/landmarks that can contribute to accuracy of ANN model. With this purpose we have started our search with the 153 possible distances among 18 critical candidate points/landmarks. The 16 of these distances having significant contribution to the model accuracy was selected by using the correlation-based feature subset selection (CFS) method. The results indicated that our ANN model has 91.2% correct classification rate.