A Robust Hand Recognition In Varying Illumination

As ubiquitous computing provide up-graded smart environments where humans desire to create various types of interaction for many kinds of media and information, the research in the area of Human-Computer Interaction (HCI) is being emphasized to satisfy a more convenient user interface. In particular, the gesture interaction technique has been one of the important research areas under ubiquitous computing environment since it can only utilize widespread consumer video cameras and computer vision techniques without the aid of any other devices to grasp human movements and intentions(Park, 2004; Jung, 2007). Among the gesture interaction techniques, recognition of hand poses and gestures has especially received attention due to great potential to build various and user-centric computer interfaces. The applicability of hand pose recognition is very high in applications where system users can not use existing interface devices such as a keyboard and a mouse since they are required to wear heavy protective gloves for industrial processes. Various types of gesture interfaces have been also presented in three-dimensional games based on virtual reality and these interfaces have enhanced an interest level and creativity within these environments for the users. Humans can distinguish hand poses very quickly through their complex optical systems, while it is very difficult for a computer system to rapidly and accurately understand hand poses. Therefore, many researchers have tried to simulate the human optical system, which can extract objects of interest from complex scenes and understand the context among objects. One of the major factors that disturb automatic gesture recognition is illumination change. The sudden illumination changes lead to the misunderstanding of background and foreground regions. We propose a robust hand recognition technique that can stably extract hand contours even under sudden illumination changes. Figure 1 shows the flowchart for our proposed method. The proposed method acquires the background images for a restricted duration and calculates the mean and standard deviation for the hue and hue-gradient of each pixel within the captured background images. That is, a background model for each pixel is built. The hue and hue-gradient of the input images captured in real-time are calculated and compared to those of the background images. The foreground objects are extracted based on the difference magnitude between those of the input image and the background image. To accurately extract the tight object region of interest, we calculate the eigen value and eigen