Emotion Recognition Using Facial Expressions with Active Appearance Models

Recognizing emotion using facial expressions is a key element in human communication. In this paper we discuss a framework for the classification of emotional states, based on still images of the face. The technique we present involves the creation of an active appearance model (AAM) trained on face images from a publicly available database to represent shape and texture variation key to expression recognition. Parameters from the AAM are used as features for a classification scheme that is able to successfully identify faces related to the six universal emotions. The results of our study demonstrate the effectiveness of AAMs in capturing the important facial structure for expression identification and also help suggest a framework for future development.