M.S. Ratliff and E. Patterson (USA)
Emotion, Facial Expression, Expression Recognition, Ac tive Appearance Model
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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