Adaptive Skin Color Detection Based on Human Face under Complex Background

Traditional skin color detection technique has a weak anti-interference ability against the pixels with color similar to skin under a complex background, and cannot reduce the influence of illumination on the characteristics of skin color. In this study, an adaptive skin color detection method is proposed to tackle the issues. The skin section containing illumination information is extracted by combining the face detection methods proposed by Haar and Adaboost, and using the improved binarization algorithm. Then, combining the best threshold of luminance component (Y) of skin color samples obtained after training in the YCbCr space, the improved histogram backprojection method is adopted to detect the skin color of the whole image. Experiments show that the method is robust under complex background and the influence of illumination. Moreover, the method has a higher accuracy and recall rate than traditional skin color detection methods.