基于深度学习的三维点云人脸识别

为了增强三维点云人脸识别系统针对多表情、多姿态的鲁棒性，提出一种基于深度学习的点云特征提取网络ResPoint。ResPoint网络使用了分组、采样和局部特征提取（ResConv）等模块，而在ResConv模块中使用了跳跃式连接，因此所提网络对于稀疏点云有很好的识别结果。首先通过人脸几何特征点定位鼻尖点，并以该点为中心切割出面部区域，切割出的区域有噪点并且有孔洞，因此对其进行高斯滤波和三维立方插值；其次，使用ResPoint网络对预处理后的点云数据提取特征；最后，在全连接层组合特征以实现三维人脸的分类。在CASIA三维人脸数据库上的实验中，与关系型卷积神经网络（RS-CNN）相比，ResPoint网络的识别正确率提高了5.06%。实验结果表明，ResPoint网络增加了网络深度的同时使用不同的卷积核提取特征，因此ResPoint网络有更好的特征提取能力。

3D point cloud face recognition based on deep learning

In order to enhance the robustness of the 3D point cloud face recognition system for multiple expressions and multiple poses, a deep learning-based point cloud feature extraction network was proposed, namely ResPoint. The modules such as grouping, sampling and local feature extraction (ResConv) were used in the ResPoint network, and skip connection was used in ResConv module, so that the proposed network had good recognition results for sparse point cloud. Firstly, the nose tip point was located by the geometric feature points of the face, and the face area was cut with this point as the center. The obtained area had noisy points and holes, so Gaussian filtering and 3D cubic interpolation were performed to it. Secondly, the ResPoint network was used to extract features of the preprocessed point cloud data. Finally, the features were combined in the fully connected layer to realize the classification of 3D faces. In the experiments on CASIA 3D face database, the recognition accuracy of the ResPoint network is increased by 5.06% compared with that of the Relation-Shape Convolutional Neural Network (RS-CNN). Experimental results show that the ResPoint network increases the depth of the network while using different convolution kernels to extract features, so that the ResPoint network has better feature extraction capability.