Facial emotional expressions recognition based on Active Shape Model and Radial Basis Function Network

Facial emotional expressions recognition (FEER) is important research fields to study how human beings reflect to environments in affective computing. With the rapid development of multimedia technology especially image processing, facial emotional expressions recognition researchers have achieved many useful result. 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. The accuracy of ASM depends on several factors, such as brightness, image sharpness, and noise. To get better result, the ASM is combined with Gaussian Pyramid. In this paper we propose a facial emotion expressions recognizing method based on ASM and Radial Basis Function Network (RBFN). Firstly, facial feature should be extracted to get emotional information from the region, but this paper use ASM method by the reconstructed facial shape. Second stage is to classify the facial emotion expressions from the emotional information. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotional expressions by using RBFN. The experimental result from RBFN classifiers show a recognition accuracy of 90.73% for facial emotional expressions using the proposed method.