This is only made robust through the estimation of their covariance. Learning to associate faces across views in vector space of similarities to prototypes . In British Machine Vision Conference, volume 1, pages 54-64, Southampton, UK, September 1998. Introduction Tracking people across a variety of views is becoming increasingly important in computer vision systems. The ability to extract visual cues such as gait or head orientation allows advanced vision .
Similarity measures can be noisy and sensitive to the choice of representation.
In order to perform accurate and robust face tracking across views, its generalisation to novel views is aided by pose estimation. Learning to associate faces across views in vector space of similarities to prototypes . It implicitly requires pose invariant face detection. Here we propose to track both head pose and face position by fusing similarity measures and skin colour information. This is only made possible through the estimation of covariance.