Facial expression recognition using FAPs-based 3DMMM

Abstract

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). In this work, modular PCA approach is used. A face is divided into six modules according to different facial features which are categorized based on Facial Animation Parameters (FAP). Each region will be treated separately in the PCA analysis. Our work is about recognizing the six basic facial expressions, provided that the properties of a facial expression are satisfied. Given a 2D image of a subject with facial expression, a matched 3D model for the image is found by fitting them to our 3D MMM. The fitting is done according to the modules; it will be in order of the importance modules in facial expression recognition (FER). Each module is assigned a weighting factor based on their position in priority list. The modules are combined and we can recognize the facial expression by measuring the similarity (mean square error) between input image and the reconstructed 3D face model.