Skin Color Profile Capture for Scale and Rotation Invariant Hand Gesture Recognition
Gesture-Based Human-Computer Interaction and Simulation
Stream processing for fast and efficient rotated Haar-like features using rotated integral images
International Journal of Intelligent Systems Technologies and Applications
Multiple classifier object detection with confidence measures
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Vision-based infotainment user determination by hand recognition for driver assistance
IEEE Transactions on Intelligent Transportation Systems
Fast and accurate hand pose detection for human-robot interaction
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Empirical study of a vision-based depth-sensitive human-computer interaction system
Proceedings of the 10th asia pacific conference on Computer human interaction
Hand gesture-based visual user interface for infotainment
Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Framework for reliable, real-time facial expression recognition for low resolution images
Pattern Recognition Letters
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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The research described in this paper analyzes the in-plane rotational robustness of the Viola-Jones object detection method when used for hand appearance detection. We determine the rotational bounds for training and detection for achieving undiminished performance without an increase in classifier complexity. The result - up to 15° total - differs from the method's performance on faces (30° total). We found that randomly rotating the training data within these bounds allows for detection rates about one order of magnitude better than those trained on strictly aligned data. The implications of the results effect both savings in training costs as well as increased naturalness and comfort of vision-based hand gesture interfaces.