Development of the facial feature extraction and emotion recognition method based on ASM and Bayesian network

In the facial image, emotions are most widely represented with eye and mouth expressions. If we want to recognize the human´s emotion via the facial image, we need to extract features of the facial image. Active Shape Model (ASM) is one of the most popular methods for facial feature extraction. Regarding the traditional ASM depends on the setting of the initial parameters of the model, in this paper we propose a facial emotion recognizing method based on ASM and Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape of the new image and calculate the initial parameters of the ASM by the reconstructed facial shape. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.