肤色过滤不良图像的方法对于皮肤裸露较多但不含关键部位或类肤色区域较多的图像容易产生误检，为此设计了一种基于人体关键部位的不良图像过滤系统. 首先提取人体关键部位灰度分布的Haar-like特征，采用Adaboost学习算法训练得到人体关键部位分类器；然后通过此分类器得到人体关键部位候选区域，提取其梯度
直方图特征、基于灰度共生矩阵的纹理特征和基于颜色矩的颜色特征，使用支持向量机(support vector machine,SVM)进行训练；最后将训练得到的SVM分类器二次过滤人体关键部位，以提高系统整体的精度. 实验结果表明，该系统能准确地检测出人体关键部位，有效地降低不良图像的误检率.
Non-pornographic images containing large naked skin or skin-like areas may be detected as pornographic using ordinary pornographic image-filtering method that heavily depend on skin detection. We design a different pornographic image-filtering system based on body parts. The system extracts Haar-like features describing local grayscale distribution of the body parts, and uses these features to train and obtain a classifier for body parts using the Adaboost learning algorithm. The candidate body part areas can be obtained with the classifier. To further improve the system performance, we extract histogram of oriented gradient features,textual features based on gray level co-occurrence matrix, and color moment features of the candidates. These
features are then used to train an support vector machine (SVM) classifier. Experiments show that the system can precisely detect key body parts in images, and therefore can effectively reduce false detection rate against non-pornographic images.
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