Facial Features Tracking for Gross Head Movement analysis and Expression Recognition

Summary form only given. The tracking and recognition of facial expressions from a single cameras is an important and challenging problem. We present a real-time framework for Action Units(AU)/Expression recognition based on facial features tracking and Adaboost. Accurate facial feature tracking is challenging due to changes in illumination, skin color variations, possible large head rotations, partial occlusions and fast head movements. We use models based on Active Shapes to localize facial features on the face in a generic pose. Shapes of facial features undergo non-linear transformation as the head rotates from frontal view to profile view. We learn the non-linear shape manifold as multiple-overlapping subspaces with different subspaces representing different head poses. The face alignment is done by searching over the non-linear shape manifold and aligning the landmark points to the features' boundaries. The recognized features are tracked across multiple frames using KLT Tracker by constraining the shape to lie on the non-linear manifold. Our tracking framework has been successfully used for detecting both gross head movements, like nodding, shaking and head pose prediction. Further, we use the tracked features to accurately extract bounded faces in a video sequence and use it for recognizing facial expressions. Our approach is based on coded dynamical features. In order to capture the dynamic characteristics of facial events, we design the dynamic haar-like features to represent the temporal variations of facial events. Inspired by the binary pattern coding, we further encode the dynamic haar-like features into binary pattern features, which are useful to construct weak classifiers for boosting learning. Finally Adaboost is used to learn a set of discriminating coded dynamic features for facial active units and expression recognition. We have achieved approximately 97% detection rate for gross head movements like shaking and nodding. The recognition rates for facial expressions averages to -95% for the most important action units.