Using affine correspondence to estimate 3-D facial pose

We describe a method for tracking a person's face through an image sequence and estimating the 3-D facial pose within each frame. It is based on an affine approximation to the motion of projected facial features such as eyes, mouth and nose. Tracking stability is maintained by enforcing the affine relationship amongst the motion of the features using linear regression and a Kalman filter. Facial pose is estimated using an ellipse-circle correspondence technique based on the affine transformation between the features in the current view and those in a fronto-parallel view. The method has the advantage of being simple to implement and not relying on assumed facial characteristics.