Blocking Adult Images Based on Statistical Skin Detection

This work is aimed at the detection of adult images that appear in Internet. Skin detection is of the paramount importance in the detection of adult images. We build a maximum entropy model for this task. This model, called the First Order Model in this paper, is subject to constraints on the color gradients of neighboring pixels. Parameter estimation as well as optimization cannot be tackled without approximations. With Bethe tree approximation, parameter estimation is eradicated and the Belief Propagation algorithm permits to obtain exact and fast solution for skin probabilities at pixel locations. We show by the Receiver Operating Characteristics (ROC) curves that our skin detection improves the performance over previous work [6] in the context of skin pixel detection rate and false positive rate. The output of skin detection is a grayscale skin map with the gray level indicating the belief of skin. We then calculate 9 simple features from this map which form a feature vector. Most of these features are based on fit ellipses, which are used to catch the characteristics of detected skin regions. Two fit ellipses are used for each skin map—the fit ellipse of all skin regions and the fit ellipse of the largest skin region. They are called respectively Global Fit Ellipse and Local Fit Ellipse in this paper. A multi-layer perceptron classifier is then trained for these features. Plenty of experimental results are presented, including photographs and a ROC curve calculated over a test set of 5, 084 photographs, which show stimulating performance for such simple features.