A novel skin segmentation scheme based on human face illumination feature is proposed in this paper. First, we perform a face detection in order to measure the face position and amount on image, and then analyze each face's illumination feature. According these parameters, we classify the skin pixel and generate skin probability map, and finally fetch out a prefect skin mask for skin segmentation. Experimental results based on common dataset show that the proposed method can achieve 92.83% true positive rate (TPR) with 15.82% false positive rate (FPR), outperforming the traditional GMM skin segmentation method. © 2012 IEEE.