Matrix computations (3rd ed.)
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Real-time tracking of image regions with changes in geometry and illumination
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Framework for Modeling Appearance Change in Image Sequences
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Updating the singular value decomposition
Journal of Computational and Applied Mathematics
A detector tree of boosted classifiers for real-time object detection and tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Efficient illumination independent appearance-based face tracking
Image and Vision Computing
Generic vs. person specific active appearance models
Image and Vision Computing
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Object Tracking Based on an Effective Appearance Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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Facial appearance changes in video sequences represent a nonstationary data problem, because of factors such as variation in pose, illumination and facial expressions. While most algorithm, that employ fixed appearance models of the target object, are not robust to track objects in uncontrolled environments. Existing Adaptive Appearance Models (AAMs) approaches solve this problem to an extent. However, they do not adequately track facial feature points, such as those relating to the eyes or mouth in the presence of significant expression changes. In this paper, we propose a method to combine an online and an offline learning for robust tracking of facial feature points. Our method firstly estimates facial feature points globally with a stochastic approach which allows to escape from local minimum. We then refine the feature points with a deterministic approach. The tracked results are filtered by offline learning approach to ensure rejection of poorly aligned targets. This allows the proposed tracker to significantly improves robustness against appearance changes and occlusions. Experiment results on tracking facial feature points in long video sequences with a wide range of facial expressions in head movement demonstrate the effectiveness and robustness of our tracker.