目的 针对形变模型方法中模型构建的缺陷，提出一种基于压缩感知理论的快速3维人脸重建方法。方法 首先，利用压缩感知理论估计3维原型人脸与目标人脸的形状相似性，根据相似性对原型样本进行筛选并构建相应的形变模型；然后，利用面部特征点信息进行稀疏模型匹配，并结合径向基函数插值重建生成特定的3维人脸。结果 在BJUT 3维数据库和CAS-PEAL 2维数据库上的实验结果表明，该方法的重建精度和重建速度均优于经典方法，重建人脸具有较强真实感。结论 该方法利用压缩感知理论快速筛选原型样本构建形变模型，有效地提高了建模精度和效率；结合RBF插值的重建策略进一步提高了重建表面的平滑度。

Objective To deal with the modeling limitation in traditional morphable models, we present a compressed sensing based method for 3D face reconstruction. Method In the proposed framework, compressed sensing is first used to estimate the similarity between testing and prototype face samples, and a modified morphable model is then built on the selected prototype samples with larger similarity. Second, the model is registered to a test face image by using facial salient points. Finally, combining the shape recovered by the modified model and the shape obtained by RBFs interpolation, the testing shape is reconstructed. Result Experiments on the BJTU-3D face database and CAS-PEAL 2D face database show that the proposed method outperforms the traditional methods in both reconstruction accuracy and computation complexity. Moreover, its recovered 3D faces achieve higher realistic impressions. Conclusion This paper presents a novel fast 3D face reconstruction method. The proposed modeling plan, based on selected prototype samples using compressed sensing, can effectively improve modeling precision and efficiency. Combined with the RBF smooth scheme, the proposed method can reconstruct face shape with high smoothness.