A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Robust Detection and Tracking of Human Faces with an Active Camera
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
A real-time face tracker for color video
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
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Face tracking in realistic environments is a difficult problem due to pose variations, occlusions of objects, illumination changes and cluttered background, among others. The paper presents a robust and real-time face tracking algorithm. A novel likelihood is developed based on a boosted multi-view face detector to characterize the structure information. The likelihood function is further integrated with particle filter which can maintain multiple hypotheses. The algorithm proposed is able to track faces in different poses, and is robust to temporary occlusions, illumination changes and complex background. In addition, it enjoys a real-time implementation. Experiments with a challenging image sequence shows the effectiveness of the algorithm.