Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Machine Vision and Applications
Fast rotation invariant multi-view face detection based on real adaboost
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Hand gesture recognition using depth data
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A boosted classifier tree for hand shape detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Learning 2D hand shapes using the topology preservation model GNG
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Multifactor feature extraction for human movement recognition
Computer Vision and Image Understanding
A modular approach to gesture recognition for interaction with a domestic service robot
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Gravity optimised particle filter for hand tracking
Pattern Recognition
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In this article a robust and real-time hand gesture detection and recognition system for dynamic environments is proposed. The system is based on the use of boosted classifiers for the detection of hands and the recognition of gestures, together with the use of skin segmentation and hand tracking procedures. The main novelty of the proposed approach is the use of innovative training techniques - active learning and bootstrap -, which allow obtaining a much better performance than similar boosting-based systems, in terms of detection rate, number of false positives and processing time. In addition, the robustness of the system is increased due to the use of an adaptive skin model, a colorbased hand tracking, and a multi-gesture classification tree. The system performance is validated in real video sequences.