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 skincolor 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...

• Type: Conference
• Year: 2004
• Where: FGR
• Authors: Qiang Zhu, Kwang-Ting Cheng, Ching-Tung Wu, Yi-Leh Wu
• Keywords: Biometrics, FGR 2004, Generic Skin Model, Rough Skin Classification, Skin-Similar Space