【摘要】 3D人脸识别是计算机视觉领域的重要组成部分，Pointnet依靠深度学习解决了点云的无序性，实现了3D点云的全局特征提取，但由于点云数据缺乏细节纹理，仅靠全局特征很难实现复杂情况下的人脸识别。针对以上问题，基于Pointnet提出了一种局部特征描述子，用于描述点云局部空间的几何特征，并引入关键特征增强机制，通过特征概率分布增强人脸关键信息，该机制能减少不必要特征对任务的干扰，有效提升模型的准确率。在公共数据集CASIA-3D、Lock3DFace、Bosphorus上进行实验测试，结果表明该方法能很好地应对表情变化、部分遮挡以及头部姿态的干扰，在弱光环境下其准确率高于RP-Net 1.1%，并具有良好的实时性。更多还原

【Abstract】 3D face recognition is an important part of the field of computer vision. Pointnet relies on deep learning to solve the disorder of point clouds and realize the global feature extraction. However, due to the lack of detailed texture of point clouds, it is difficult to realize face recognition in complex situations only by global features. In deal with the above problems, a local feature descriptor is proposed to describe the local spatial geometric features of the point clouds, and a key feature enhancement mechanism is introduced to enhance the key features of the face through the probability distribution, which can reduce the interference of unnecessary features and effectively improve the accuracy of the model. Experiments were carried out on public data sets CASIA-3D,Lock3DFace and Bosphorus. The results show that our method can deal well with the change of expression, partial occlusion and interference of head pose, especially in weak light conditions, compared with RP-Net, the accuracy is improved by 1.1 percent, and the method also has good real-time performance.更多还原