3D head tracking using non-linear optimization

Accurate and reliable tracking of the 3D position of human heads is a continuing research problem in computer vision. This paper addresses the specific problem of model-based tracking with a generic deformable 3D head model. Following the work of Vetter and Blanz, a collection of head models is obtained from a 3D scanner, registered and parameterized to give a generic head model which is linearly parameterized by a small number of parameters. This is the 3D analogue of Cootes and Taylor’s active appearance models. We cast tracking as a parameter estimation problem, and note that many existing solutions to the problem—such as CONDENSATION and Kalman filtering—are analogous to nonlinear optimization strategies in numerical analysis. We show how careful analysis of the error function, parameterization of the model pose parameters, and choice of optimizer allows us to robustly track 3D head pose in digital video camera footage of quickly moving heads.