为了探测图像中的肤色像素，提出了一种新的方法－支持向量机(SVM：Support Vector Machine)方法．它是一种基于肤色的非特定人的面部定位方法，是非接触人机交互技术和机器视觉中的一个重要内容．实验结果表明，采用支持向量机方法较传统人工神经网络方法不仅有更高的探测准确性，而且具有更好的推广性能．由于SVM采用结构风险最小化(SRM：Structural Risk Minimization)准则，在最小化训练误差(经验风险)的同时，尽量缩小模型预测误差的上界，从而使模型有更好的泛化能力．

A detection method of complexion based on SVM is proposed in this paper. The technique is an approach for locating faces in a scene image based on the detection of skin color of an unspecific human which is essentially important in development of contact free human-machine-interaction (HMI) as well as in machine visions. The simulation results show that it may not only get better rate of correct recognition of complexion than that by using traditional artificial neural network but also is better in generalization.This is because that according the criteria of structural risk minimization of support vector machine(SVM),the errors between sample-data and model-data are minimized and the upper bound of predicting error of the model is also decreased simultaneously.