Improving appearance-based 3D face tracking using sparse stereo data

Recently, researchers proposed deterministic and statistical appearance-based 3D head tracking methods which can successfully tackle the image variability and drift problems. However, appearance-based methods dedicated to 3D head tracking may suffer from inaccuracies since these methods are not very sensitive to out-of-plane motion variations. On the other hand, the use of dense 3D facial data provided by a stereo rig or a range sensor can provide very accurate 3D head motions/poses. However, this paradigm requires either an accurate facial feature extraction or a computationally expensive registration technique (e.g., the Iterative Closest Point algorithm). In this paper, we improve our appearance-based 3D face tracker by combining an adaptive appearance model with a robust 3D-to-3D registration technique that uses sparse stereo data. The resulting 3D face tracker combines the advantages of both appearance-based trackers and 3D data-based trackers while keeping the CPU time very close to that required by real-time trackers. We provide experiments and performance evaluation which show the feasibility and usefulness of the proposed approach.