Real-time facial expression recognition using STAAM and layered GDA classifier

摘要

This paper proposes a real-time person independent facial expression recognition in two parts: one is a model fitting part using a proposed stereo active appearance model (STAAM) and another is a person independent facial expression recognition using a layered generalized discriminant analysis (GDA) classifier. The STAAM fitting algorithm uses multiple calibrated perspective cameras to compute the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. The STAAM uses a modified simultaneous update fitting method that reduces the fitting computation greatly. Also, the layered GDA classifier combines 3D shape and 2D appearance to improve the recognition performance of person independent facial expressions. Experimental results show that (1) the STAAM shows a better fitting stability than the existing multiple-view AAM (MVAAM), (2) the modified simultaneous update algorithm accelerates the AAM fitting speed, and (3) the combination of the 3D shape and 2D appearance features using a layered GDA classifier improves the performance of facial expression recognition greatly.