Adaptive learning of an accurate skin-color model

Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture the wide distribution of skin colors in individual images. In this paper, we propose an adaptive skin-detection method, which allows modeling true skin-color distribution with significantly higher accuracy and flexibility than other methods attain. In principle, the proposed method follows a two-step process. For a given image, we first perform a rough skin classification using a generic skin model which defines the Skin-Similar space. In the second step, a Gaussian Mixture Model (GMM), specific to the image under consideration and refined from the Skin-Similar space, is derived using the standard Expectation-Maximization (EM) algorithm. Then, we use an SVM (Support Vector Machine) classifier to identify the skin Gaussian from the trained GMM (which contains two Gaussian components) by incorporating spatial and shape information of the skin pixels. This adaptive method can be applied to both still images and video applications. Results of extensive experiments performed on live video sequences and large image databases have demonstrated the effectiveness and benefits of the proposed model.