Skin Detection Based on Image Color Segmentation with Histogram and K-Means Clustering

Skin detection is a crucial pre-processing step for finding human faces in images. The challenging task is to find a reliable, yet efficient method for detection of skin region(s). In this paper, we proposed a new, simple and efficient method for skin detection based on image segmentation of different color spaces, and simple clustering technique (K-means) for clustering similar pixels on an image. Three K-means input features are used : a) two components from two different color spaces (Hue, Cr, Cb), b) positions of pixels on an image and c) rough estimation of skin pixels obtained from skin-color based detection. Our approach showed promising results on human images from different ethnicities, with simple background and high illumination. The computational cost of the method has been very low, since no training data is required. Results indicate that the method is suitable as a pre-processing step for some supervised method for advanced human skin segmentation and detection.