Emotion recognition using facial expressions with active appearance models

Abstract

Recognizing emotion using facial expressions is a key ele-ment 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 in-volves 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 fea-tures for a classification scheme that is able to successfully identify faces related to the six universal emotions. The re-sults 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.

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