Facial Feature Detection and Tracking with a 3D Constrained

Abstract

In this paper, we describe a system for facial feature detection and tracking using a 3D extension of the Constrained Local Model (CLM) [Cris 06, Cris 08] algorithm. The use of a 3D shape model allows improved tracking through large head rotations. CLM uses a joint shape and texture appearance model to generate a set of region template detectors. A search is then performed in the global pose / shape space using these detectors. The proposed extension uses multiple appearance models from different viewpoints and a single 3D shape model. During fitting or tracking the current estimate of pose is used to select the appropriate appearance model. We demonstrate our results by fitting the model to image sequences with large head rotations. The results show that the proposed 3D constrained local model algorithm improves the performance of the original CLM algorithm for videos with large out-of-plane head rotations.