Image and Video Compression Standards: Algorithms and Architectures
Image and Video Compression Standards: Algorithms and Architectures
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Facial action tracking using particle filters and active appearance models
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Journal of Cognitive Neuroscience
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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We propose an efficient real-time face tracking system that can track fast moving face and cope with the illumination changes. To achieve these goals, we use the active appearance model(AAM) to represent the face image due to its simplicity and flexibility and take the particle filter framework to track the face image due to its robustness. We modify the particle filter framework as follows. To track fast moving face, we predict the motions using motion history and motion estimation, hence we can reduce the required number of particles. For observation model, we use active appearance model(AAM) to obtain an accurate face region, and update the model using incremental principle component analysis(IPCA). Occlusion handling scheme incorporates motion history to handle the moving face with occlusion. We have expanded our application to multiple faces tracking system. Experimental results present the robustness and effectiveness of the proposed system.