Vision-based skin-colour segmentation of moving hands for real-time applications

We present a robust vision-based skin-colour segmentation method for moving hands in a real-time application. Segmentation of hands is an important processing step in gesture recognition applications, where the general shape and position of the hands are of interest. In contrast to these approaches, the presented method concentrates on an accurate segmentation, which is required for further processing steps in a real-time videoconferencing application. A hand tracking procedure is applied to improve the segmentation in terms of accuracy, robustness and processing speed. Furthermore the presented approach can accomplish difficult situations like contact between hands or contact between face and hands. This is important for many real-time applications, e.g. for the presented videoconference system to allow the conferees a natural behaviour. Moreover we present an approach for an automatic initialisation of the skin-colour range to the specific user. We show experimental results proving the efficiency and reliability of our approach. The proposed ahnd segmentation method is capable of processing TV-sized (CCIR 601, 576 x 720 pixels)video images in real-time with 25 Hz on a common PC. The presented approach will support any video processing in visual media production, where segmentation accuracy and real-time capability is required.