We present a method for learning appearance models that can be used to recognise and track both 3D head pose and identities of novel subjects with continuous head movement across the view-sphere. We describe an automatic face data acquisition system based on a magnetic sensor and a calibrated camera. The system enabled us to obtain systematically a database of face images with labelled 3D poses across a view-sphere of 90 degrees yaw and 30 degrees tilt at intervals of 10 degrees. The database was used to learn appearance models of unseen faces based on similarity measures to prototype faces. The method is computationally efficient and enables real-time performance.
Keywords:
Face Recognition, Pose Tracking, View-Based Representation, Similarity to Prototypes, Pose Generalisation, Face Database
