An adaptive skin model and its application to objectionable image filtering

We propose an adaptive skin-detection method, which allows modelling and detection of the true skin-color pixels with significantly higher accuracy and flexibility than previous methods. In principle, the proposed approach 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. The Skin-Similar space often contains many non-skin pixels due to the inevitable overlap in the color space between skin pixels and some non-skin pixels under the generic skin-model. The objective of the second step is to reduce the false-positive rate by analyzing the image under consideration. Specifically, in the second step, a Gaussian Mixture Model (GMM), specific to the image under consideration and refined from its Skin-Similar space, is derived using the standard Expectation-Maximization (EM) algorithm. We then use a Support Vector Machine (SVM) classifier to identify the skin Gaussian from the ...

• Type: Conference
• Year: 2004
• Where: MM
• Authors: Qiang Zhu, Ching-Tung Wu, Kwang-Ting Cheng, Yi-Leh Wu
• Keywords: Gaussian Mixture Model, MM 2004, Skin Pixels, Skin-Similar Space