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Abstract: A color based system using multiple templates was developed and implemented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color statistics. Then it separates skin regions from non-skin regions. After that, it locates the frontal human face(s) within the skin regions. In the first step, 250 skin samples from persons of different ethnicities are used to determine the color distribution of human skin in chromatic color space in order to get a chroma chart showing likelihoods of skin colors. This chroma chart is used to generate, from the original color image, a gray scale image whose gray value at a pixel shows its likelihood of representing the skin. The algorithm uses an adaptive thresholding process to achieve the optimal threshold value for dividing the gray scale image into separate skin regions from non skin regions. Finally, multiple face templates matching is used to determine if a given skin region represents a frontal human face or not. Test of the system with more than 400 color images showed that the resulting detection rate was 83%, which is better than most color-based face detection systems. The average speed for face detection is 0.8 second/image (400×300 pixels) on a Pentium 3 (800MHz) PC.

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