基于模糊聚类的肤色分割

肤色是彩色图像人脸检测中一个非常重要的特征。通常采用一个统计模型分割出可能的肤色区域，但往往会有很多误判。此外，CbCr等简单的二维空间，不能表示真正的肤色分布。该文提出采用三维的CrCbCg模型来更精确地描述肤色分布，同时考虑到一幅图像中肤色区域内颜色点的分布具有相对稳定的特点，利用一种模糊聚类的方法对CrCbCg模型的输出结果进行二次分割，进一步去除非肤色点。由于结合了每幅图像自身的特点，该算法能大大提高肤色分割结果的准确性。大量实验结果表明，该算法能有效处理95%以上的彩色图像，对于70%以上的图像可得到很好的分割结果。

Skin Extraction Based on Fuzzy Cluster

Skin color is an important feature for face detection in color images.Usually by applying a statistical skin model,possible skin region can be segmented.However,the output can not be as accurate as expected.Besides,simple 2D model,e.g.CbCr,can not present the real skin point distribution.So this paper presents a 3D CrCbCg model to describe skin distribution more precisely.Meanwhile,considering skin points in a specific image have a relative stable distribution, a cluster-based skin model is presented to remove background points which are wrongly retained by the general model. Because of applying the characteristic of each specific image,this algorithm can effectively improve the performance and accuracy of skin model.Experimental result shows that this algorithm can achieve satisfied results for over 95% images, including obviously improved images for over 70%.