Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Real-Time Animation of Realistic Virtual Humans
IEEE Computer Graphics and Applications
Real-Time Fingertip Tracking and Gesture Recognition
IEEE Computer Graphics and Applications
Vision-Based Gesture Recognition: A Review
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Detection and Estimation of Pointing Gestures in Dense Disparity Maps
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The Hand Mouse: GMM Hand-Color Classication and Mean Shift Tracking
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Arm-Pointing Gesture Interface Using Surrounded Stereo Cameras System
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A survey of skin-color modeling and detection methods
Pattern Recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Visual capture and understanding of hand pointing actions in a 3-D environment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A method for hand detection using internal features and active boosting-based learning
Proceedings of the Fourth Symposium on Information and Communication Technology
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Hand gestures are an efficient manner for human computer interaction (HCI). They can also be used for the development of a non-intrusive biometrics system. In this paper, we address the issues of hand detection and gesture tracking using a single camera. A simple yet effective approach is proposed for applications with complex backgrounds and minimal constraints on the subject. A hand detection approach is presented using a Bayesian classifier based on Gaussian Mixture Models (GMM) for identifying pixels of skin color. A connected component based region-growing algorithm is included for forming areas of skin pixels into areas of likely hand candidates. Given the detected hand region, we further detect the hand features using a deformable model for hand gesture estimation. We propose a novel method, a 3D physics-based dynamic mesh adaptation approach, to estimate and track hand shape and finger directions. The physics-based hand model adaptation algorithm allows us to model hand shape and orientation at the same time, thereby improving the robustness and speed for hand gesture tracking and regeneration.