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A rotation invariant human face detection system in color images based on human skin color distribution and intensity is proposed in this paper. Skin color distribution typical to a human face is used as a feature along with the intensity variations to classify the candidate regions into faces and nonfaces. The detection process is carried out in YCbCr color space. Sparse Network of Winnows architecture is used to train three networks one for intensity and two for the color distributions for classification of candidate regions. Rotation invariance in detection of faces is achieved by training multiple classifiers, each to detect faces at a particular orientation. The detection process also implements a non linear luminance based lighting compensation method which is very efficient in enhancing and restoring the natural colors into the images which are taken in darker and varying lighting conditions. Experimental results show that the new face detection technique is highly efficient in terms of speed and accuracy in detecting frontal view faces at different orientations in complex environments.