基于特征分块的三维人脸重建和识别

针对传统三维人脸重建算法效率低且难以满足实际应用的缺陷，提出一种基于特征分块的三维人脸重建算法，并将此算法应用到三维人脸识别中，实现了基于特征分块的加权三维人脸识别。首先，利用基于平面模板的非均匀重采样法对原始数据进行归一化；其次，采用主动形状模型(ASM)算法对三维人脸和二维人脸图像进行特征定位和特征分块；然后，利用基于分块主元分析(PCA)的稀疏形变模型算法实现每个人脸分块的三维重建；最后，实现了此算法在三维人脸识别中的应用。实验表明，此重建算法具有较高的精度和重建效率，还可以达到全局最优，并且可以提高三维人脸的识别率。

3D face reconstruction and recognition based on feature division

The traditional algorithm of 3D face reconstruction is inefficient and it is difficult to meet the requirements of practical application. To address this problem, a feature-slice-based 3D face reconstruction algorithm was proposed. Besides, the feature-slice-based weighed 3D face recognition was proposed on the basis of the reconstruction algorithm. First, a 2D template-based alignment algorithm was developed to process the correspondence between faces automatically, and a linear facial model was built up. Second, an improved Active Shape Model (ASM) algorithm was proposed to locate the feature points and slices in the 3D and 2D face images. Then, every facial feature slices shape was reconstructed by a PCA-based sparse morphable mode. Finally, the algorithm was applied to 3D face recognition. The experimental results show that the presented algorithm has higher efficiency and accuracy, and improves the 3D face recognition rate.