Using Detailed Independent 3D sub-models to improve facial feature localisation and pose estimation.

We show that the results from searching 2D images or a video sequence, with a 3D head model can be improved by using detailed sub-models. These parts are initialised with the full model result and are allowed to search independently of that model, and of each other, using the same algorithm. The final results for the sub-models can be reported exactly, or optionally fed back into the full model to be constrained by its parameter space. In the case of a video sequence this can then be used in the initialisation of the next frame. We tested various data sets, constrained and unconstrained, including a variety of lighting conditions, poses, and expressions. Our investigation showed that using the sub-models improved on the original full model result on all but one of the data sets. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218213013600178