Facial expression recognition using continuous dynamic programming

Abstract

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.