Robust regression and outlier detection
Robust regression and outlier detection
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Real-Time Head Tracking Fusing Color Histograms and Stereovision
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Active Appearance Models Revisited
International Journal of Computer Vision
An active model for facial feature tracking
EURASIP Journal on Applied Signal Processing
Robust real-time 3D head pose estimation from range data
Pattern Recognition
On Appearance Based Face and Facial Action Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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Recently, researchers proposed deterministic and statistical appearance-based 3D face tracking methods that can successfully tackle the image variability and drift problems. However, appearance-based methods dedicated to 3D face tracking may suffer from inaccuracies since they 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 face 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 propose a 3D face tracker that is based on appearance registration and on a fast variant of a robust Iterative Closest Point algorithm. The resulting 3D face tracker combines the advantages of both appearance-based trackers and 3D data-based trackers. Experiments on real video data show the feasibility and usefulness of the proposed approach.