Facial Expression Recognition Using Active Appearance Models

A framework for automatic facial expression recognition combining Active Appearance Model (AAM) and Linear Discriminant Analysis (LDA) is proposed. Seven different expressions of several subjects, representing the neutral face and the facial emotions of happiness, sadness, surprise, anger, fear and disgust were analysed. The proposed solution starts by describing the human face by an AAM model, projecting the appearance results to a Fisherspace using LDA to emphasize the different expression categories. Finaly the performed classification is based on malahanobis distance.

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