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The problem of human face detection in a natural or artificial environment has always been among the highest priorities for researchers working in the field of computer vision systems and artificial intelligence. An effective face detection system should provide high percentage of correct detections, and low false detection rate in short time. Viola-Jones method is one of the best algorithms in terms of speed/quality ratio. However, this method in many cases gives a large number of false detections. The color of human skin is one of the features that helps to make face detection. The presence of the color information improves the efficiency of face allocation; narrows the search area and reduces the number of false detections and processing time of the input images. This paper solved is the problem face area detection, based on two new methods. The first technique uses the image pixel skipping process instead of testing each pixel to label it as skin or non-skin by using RGB color space. Second technique uses YCbCr color space and block approach which divides the image into blocks, the size of each block is 3×3 pixels, then we check the central pixel. If the central pixel satisfies the skin criteria, the whole block will be considered as a skin. Finally, we applied Viola-Jones algorithm to detect faces. The experimental results presented in the paper shows that the proposed algorithms provide high speed detection at low false error rate. |
