Qiang Zhu
ABSTRACT
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 trained GMM by incorporating spatial and shape information of the skin pixels. Moreover, we examine how the improvement on skin detection by this adaptive skin-model impacts the detection accuracy in the application of Objectionable Image Filtering. We further propose a two-level classification scheme based on hierarchical bagging to improve the accuracy. Results of extensive experiments on large databases demonstrate the effectiveness and benefits of our adaptive skin-model.
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Index Terms
- An adaptive skin model and its application to objectionable image filtering
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