Facial expression recognition using continuous dynamic programming

Describes an approach to facial expression recognition (FER). We represent facial expressions by a facial motion graph (FMG), which is based on feature points and muscle movements. FER is achieved by analyzing the similarity between an unknown expression´s FMG and FMG models of known expressions by employing continuous dynamic programming. Furthermore we propose a method to evaluate edge weights in FMG similarity calculation, and use these edge weights to achieve a more accurate and robust system. Experiments show the excellent performance of this system on our video database, which contains video data captured under various conditions with multiple motion patterns