We describe a method for tracking a person's face through an image sequence and estimating the 3-D facial pose within each frame. The technique 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 application of a Kalman filter to the estimated affine parameters. 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. Experiments on both synthetic and real sequences illustrate the effectiveness of the approach.