Real time hand detection in a complex background

Hand gesture recognition has gained the interest of many researchers in recent years, as it has become one of the most popular Human Computer Interfaces. The first step in most vision-based gesture recognition systems is the hand region detection and segmentation. This segmentation can be a particularly challenging task when it comes to complex backgrounds and varying illumination. In such environments, most hand detection techniques fail to obtain the exact region of the hand shape, especially in cases of dynamic gestures. Meeting these requirements becomes even more difficult, due to real-time operation demand. To overcome these problems, in this paper, we propose a new method for real-time hand detection in a complex background. We employ a combination of existing techniques, based on motion detection and introduce a novel skin color classifier to improve segmentation accuracy. Motion detection is based on image differencing and background subtraction. Skin color detection is accomplished via a color classification technique that employs online color training, so that the system can dynamically adapt to the variety of lighting conditions and the user׳s skin color as well as possible. Morphological features of the detected hand in previous frames are employed to estimate the probability of a pixel belonging to the hand section in the current frame. Finally, the derived motion, color and morphological information are combined to detect the hand region. Experimental results show significant improvement in hand region detection, compared to existing methods with an average accuracy of 98.75%.