Facial expression analysis robust to 3D head pose motion
Most face expression algorithms assume a front or 'near-to-front' head position. This assumption becomes an important limitation when studying input from real systems. We present a new approach to robustly determine face expressions independently of the head pose. Our analysis-synthesis cooperation, possible thanks to the use of a highly realistic 3D head model and the application of Kalman filtering to predict the user pose, permits us to correctly track the interesting face features. Adapting 'near-to-front' analysis techniques based on the predicted pose enables us to use such algorithms with moving speakers.