A real-time facial expression recognition system based on Active Appearance Models using gray images and edge images

In this paper, we present an approach for facial expression classification, based on Active Appearance Models. To be able to work in real-world, we applied the AAM framework on edge images, instead of gray images. This yields to more robustness against varying lighting conditions. Additionally, three different facial expression classifiers (AAM classifier set, MLP and SVM) are compared with each other. An essential advantage of the developed system is, that it is able to work in real-time - a prerequisite for the envisaged implementation on an interactive social robot. The real-time capability was achieved by a two-stage hierarchical AAM tracker and a very efficient implementation.