Hand region extraction and gesture recognition from the video stream with complex background through entropy analysis

The hand gesture recognition utilizing image processing relies upon recognition through markers or hand extraction by colors, therefore it is heavily restricted by the colors of clothes or skin. In this paper, we propose a method to recognize hand gestures extracted from images with complex background for more natural interface in HCI(Human Computer Interaction). The proposed method is obtaining the image through subtract one image from another sequential image, measuring the entropy, separating hand region from images, tracking the hand region and recognizing hand gestures. Through entropy measurement, we've got color information that have near distribution in complexion for region that have big value and extracted hand region from input images. We could draw hand region adaptively in change of lighting or individual's difference because entropy offer color information as well as motion information at the same time. Detected contour using chain code for hand region that is extracted, and present centroidal profile method that is improved little more and recognized gesture of hand. In the experimental results for 6 kinds of hand gesture, it shows the recognition rate with more than 95% for person and 90similar to100% for each gesture at 5 frames/sec.