Real-time face tracking and pose correction for face recognition using active appearance models

This paper presents a fully automatic real-time face recognition system from video by using Active Appearance Models (AAM) for fitting and tracking facial fiducial landmarks and warping the non-frontal faces into a frontal pose. By implementing a face detector for locating suitable initialization step of the AAM shape searching and fitting process, new facial images are interpreted and tracked accurately in real time (15fps). Using an Active Appearance Model (AAM) for normalizing facial images under different poses and expressions is crucial to providing improved face recognition performance as most systems degrade matching performance with even smallest pose variation. Furthermore the AAM is a more robust feature registration tracking approach as most systems detect and locate the eyes while AAMs detect and track multiple fiducial points on the face holistically. We show examples of AAM fitting and tracking and pose normalization including an illumination pre-processing step to remove specular and cast shadow illumination artifacts on the face. We show example pose normalization images as well as example matching scores showing the improved performance of this pose correction method.