Color-Based Hands Tracking System for Sign Language Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Does Colorspace Transformation Make Any Difference on Skin Detection?
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Camera-Based Gesture Recognition for Robot Control
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
A New Robust Face Detection in Color Images
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Skin Color-Based Video Segmentation under Time-Varying Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast 2D Hand Tracking with Flocks of Features and Multi-Cue Integration
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Adaptive learning of an accurate skin-color model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Skin detection using neighborhood information
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Multi-layered hand and face tracking for real-time gesture recognition
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
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Skin detection has been employed in various applications including face and hand tracking, and retrieving people in video databases. However most of the currently available algorithms are either based on static features of the skin color, or require a significant amount of computation. Moreover, skin detection algorithms are not robust enough to deal with real-world conditions, such as background noise, change of intensity and lighting effects. This situation can be improved by using dynamic features of the skin color in a sequence of images. This article proposes a skin detection algorithm based on in-motion pixels of the image. The membership measurement function for recognizing skin/non skin is based on the Hue histogram of skin pixels that adapts itself to the user's skin color, in each frame. This algorithm has demonstrated significant improvement in comparison to the static skin detection algorithms.