Active shape models—their training and application
Computer Vision and Image Understanding
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Connected Vibrations: A Modal Analysis Approach for Non-Rigid Motion Tracking
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Emerging Topics in Computer Vision
Emerging Topics in Computer Vision
Emblem Detections by Tracking Facial Features
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Robust Pose Invariant Facial Feature Detection and Tracking in Real-Time
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Real-Time Multi-View Face Detection and Pose Estimation in Video Stream
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Computer Vision and Image Understanding - Special issue on eye detection and tracking
A comparison of shape constrained facial feature detectors
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Authentic facial expression analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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Epidemiological studies indicate that automobile drivers from varying demographics are confronted by difficult driving contexts such as negotiating intersections, yielding, merging and overtaking. We aim to detect and track the face and eyes of the driver during several driving scenarios, allowing for further understanding of a driver's visual search pattern behavior. Traditionally, detection and tracking of objects in visual media has been performed using specific techniques. These techniques vary in terms of their robustness and computational cost. This research proposes a real-time framework that is built upon a foundation synonymous to boosting, which we extend from learners to trackers and demonstrate that the idea of an integrated framework employing multiple trackers is advantageous in forming a globally strong tracking methodology. In order to model the effectiveness of trackers, a confidence parameter is introduced to help minimize the errors produced by incorrect matches and allow more effective trackers with a higher confidence value to correct the perceived position of the target.