A model based method for automatic facial expression recognition

Abstract

Automatic facial expression recognition is a research topic with interesting applications in the field of human-computer interaction, psychology and product marketing. The classification accuracy for an automatic system which uses static images as input is however largely limited by the image quality, lighting conditions and the orientation of the depicted face. These problems can be partially overcome by using a holistic model based approach called the Active Appearance Model. A system will be described that can classify expressions from one of the emotional categories joy, anger, sadness, surprise, fear and disgust with remarkable accuracy. It is also able to detect smaller, local facial features based on minimal muscular movements described by the Facial Action Coding System (FACS). Finally, we show how the system can be used for expression analysis and synthesis.