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This paper introduces a method for automatic facial expression recognition in image sequences, which make use of Candide wire frame model and active appearance algorithm for tracking, and support vector machine for classification. Candide wire frame model is adapted properly on the first frame of face image sequence. Facial features in subsequent frames of image sequence are tracked using active appearance algorithm. The algorithm adapts Candide wire frame model to the face in each of the frames and tracks the grid in consecutive video frames over time. Last frame of image sequence corresponds to greatest facial expression intensity. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the support vector machine, which classifies facial expression into one of the class such as happy, surprise, sad, anger, disgust and fear.