传统迭代最近点 (Iterative Closest Point，ICP)不能很好处理异常值、噪声及丢失数据等情况，导致降低了在人脸建模中三维点云配准的精度。本文提出使用稀疏迭代最近点 (Sparse Iterative Closest Point，SICP)来进行深度图的配准，稀疏 ICP通过使用稀疏诱导准则去重新书写配准优化公式以解决这些问题从而提高配准的精度。另外，本文提出一种快速分割头部的方法，它可以从深度图中快速的分割出用户的头部，进而建立一个三维人脸建模系统。实验结果证明了本文算法的有效性。

To overcome the problem that traditional Iterative Closest Point (ICP) can’t properly handle the situation of outliers, noise and missing data appeared in the process of image processing which would result in low accuracy of image registration. An enhanced Sparse Iterative Closest Point (SICP) to register the 3D point cloud is proposed, and sparse ICP address these problems by formulating the registration optimization using sparsity inducing norms. What’s more, a fast head segmentation algorithm was proposed to segment the user’s head in depth image. Based on the proposed fast face segmentation algorithm and sparse ICP, a new 3D face modeling system is put forward. The experimental results demonstrate the effectiveness of the proposed algorithm