Facial expression recognition using MPEG-4 FAP-based 3D MMM

A 3D Modular Morphable Model (3DMMM) is introduced to deal with facial expression recognition. The 3D Morphable Model (3DMM) contains 3D shape and 2D texture information of faces extracted using conventional Principal Component Analysis (PCA). I this work, Modular PCA approach is used. A face is divided into several components according to different facial features which are categorized based on Facial Animation Parameters (FAP). FAP is the measurement of muscular action relevant to the Action Units and it provides temporal information that is needed in order to have a realistic facial expression effect. Each region will be treated separately in the PCA analysis. Our work is about recognizing the six basic emotions, provided that the properties of an emotion are satisfied.