Skin color is widely used in many applications because of its merit in human-machine interactions. However, detecting skin color requires repetitive operations on all pixels in the image, similar to other vision-based applications. Since the per-pixel processing is difficult to perform efficiently in conventional computers, many real-time image processing applications have problems with performance. In this paper, we propose FPGA implementation of a real-time skin color detection system. Among the various skin color detection methods, we chose a parametric skin distribution modeling method based on a Gaussian mixture, due to its acceptable training amount and skin detection performance. In addition, a mean-based surface flattening method was also proposed and implemented to improve the detection performance. The proposed method flattens the surface of objects in the scene by replacing the pixel value with the mean of its similar neighborhoods to remove the color noise. After this flattening process, the pixel values of the analogous adjacent pixels are located within a narrow range and are easily segmented to a different region. To consider the inherent parallelism of local image processing, all these functions are implemented within the FPGA to meet the demands of real-time performance.

We present a method to detect human fingertips from images captured by a stereo camera. The system makes use of the disparity information from a stereo camera to find candidates, and defines an evaluation process to detect two hands. The finger detector then processes each hand image to extract finger images. Finally, we perform geometric calculations on the results to relocate the positions of the fingertips. The proposed method is not complex; however, it shows exciting results in terms of run time and detection rates. The extraction result can be used in hand configuration modeling for gesture recognition in HCI systems.