3D Constrained Local Model for rigid and non-rigid facial tracking

We present 3D Constrained Local Model (CLM-Z) for robust facial feature tracking under varying pose. Our approach integrates both depth and intensity information in a common framework. We show the benefit of our CLM-Z method in both accuracy and convergence rates over regular CLM formulation through experiments on publicly available datasets. Additionally, we demonstrate a way to combine a rigid head pose tracker with CLM-Z that benefits rigid head tracking. We show better performance than the current state-of-the-art approaches in head pose tracking with our extension of the generalised adaptive view-based appearance model (GAVAM).