针对人脸特征分类问题，提出一种基于主动形状模型(ASM)和 K 近邻算法的人脸脸型分类方法。将 Hausdorff 距离作为 K 近邻算法的距离函数，利用 ASM 算法提取待测图像的特征点，对点集进行归一化后计算人脸轮廓特征点与样本库中所有样本点集的 Hausdorff距离，根据该距离值，通过 K 近邻算法实现待测图像的脸型分类。实验结果证明，该方法分类正确率高、速度快、易于实现。(Aiming at the problem of face feature classification, this paper proposes a new face classification algorithm based on Active Shape Model(ASM)　and　K-nearest　neighbor　algorithm.　It　extracts　feature　points　of　face　by　ASM　algorithm,　normalizes　all　feature　points,　and　computes Hausdorff distance between feature points and every sample of each class. The face is classified by K-nearest neighbor algorithm with the Hausdorff distance computed. Experimental results show that the algorithm has high classification accuracy and speed, and it is easy to realize.)