Abstract: This paper presents research leading to the development of a vision-based gesture recognition system. The system comprises of three abstract layers each with their own specific type and requirements of data. The first layer is the skin detection layer. One of the contributions of the research presented is an adaptive skin detection algorithm that forms the core of this layer. This component provides a set of disperse skin pixels for a tracker that forms the second layer. This second component is based on the Mean-shift algorithm which has been improved for robustness against noise using our novel fuzzy-based edge estimation method making the tracker suitable for real world applications. The third component is the gesture recognition layer which is based on a gesture modeling technique and a classification method that we have developed for this purpose. We have used the angle space to model the input gesture and artificial neural-networks for classification of the gesture.
