Model-Based Head Tracking and 3D Pose Estimation

This paper presents a generic method for addressing the issue of 3D model-based head pose estimation. The method proposed relies on the downhill simplex optimization method and on the combination of motion and texture features. A proper initialization based on a block matching procedure associated with 3D/2D matching depending on texture and optical flow information leads to an accurate recovery of the pose parameters. By using a 3D head model, the procedure takes into account the motion of the entire head and not a set of characteristic parts. Similarly, unlike feature-based methods, the whole head is tracked and no constraint by some features vanishing from view is needed. We show that the accuracy of the pose estimation is increased when considering a 3D head-like synthesized surface by using a limited Fourier expansion instead of ellipsoidal head model. We demonstrate that this method is stable over extended sequences including large head motions, occultations, various head postures and lighting variations. The method proposed is generally enough to be applied to other tracking domains.