IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametrized structure from motion for 3D adaptive feedback tracking of faces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Real Time Tracking and Modeling of Faces: An EKF-Based Analysis by Synthesis Approach
MPEOPLE '99 Proceedings of the IEEE International Workshop on Modelling People
Motion Regularization for Model-Based Head Tracking
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Fully Automated and Stable Registration for Augmented Reality Applications
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models
International Journal of Computer Vision
Integrating multiple visual cues for robust real-time 3D face tracking
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Model-assisted 3D face reconstruction from video
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Robust: real-time 3D face tracking from a monocular view
Journal on Image and Video Processing
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3D tracking of faces in video streams is a difficult problem that can be assisted with the use of a priori knowledge of the structure and appearance of the subject’s face at predefined poses (keyframes). This paper provides an extensive analysis of a state-of-the-art keyframe-based tracker: quantitatively demonstrating the dependence of tracking performance on underlying mesh accuracy, number and coverage of reliably matched feature points, and initial keyframe alignment. Tracking with a generic face mesh can introduce an erroneous bias that leads to degraded tracking performance when the subject’s out-of-plane motion is far from the set of keyframes. To reduce this bias, we show how online refinement of a rough estimate of face geometry may be used to re-estimate the 3d keyframe features, thereby mitigating sensitivities to initial keyframe inaccuracies in pose and geometry. An in-depth analysis is performed on sequences of faces with synthesized rigid head motion. Subsequent trials on real video sequences demonstrate that tracking performance is more sensitive to initial model alignment and geometry errors when fewer feature points are matched and/or do not adequately span the face. The analysis suggests several indications for most effective 3D tracking of faces in real environments.