Supervised Learning Methods for Skin Segmentation Based on Pixel Color Classification

Abstract

Over the last few years, skin segmentation has been widely applied in diverse aspects of computer vision and biometric applications including face detection, face tracking, and face/hand-gesture recognition systems. Due to its importance, we observed a reawakened interest in developing skin segmentation approaches. In this paper, we offer a comparison between five major supervised learning algorithms for skin segmentation. The algorithms involved in this comparison are: Support Vector Machines (SVM), K-Nearest-Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), and Logistic Regression (LR). Various scenarios of data pre-processing are proposed including a conversion from RGB into YCbCr color space. Using YCbCr representation gave a better performance in skin/non-skin classification. Despite the settled comparison criteria, KNN was found to be the most desirable model that provides a stable performance overall the several experiments conducted.