基于分层特征化网络的三维人脸识别

针对三维人脸多表情、多姿态变化同时存在，人脸点云数据不同程度缺失的问题，探索性地将三维点云人脸数据应用于PointNet系列的分类网络并进行了识别结果的对比与分析，然后提出了一种新的网络框架——HFN。首先，在数据预处理后随机采样固定点数的点云；其次，将固定点数的人脸点云输入SA模块，以获取局部区域的质心点、邻域点并提取局部区域的特征，然后拼接由DSA模块基于多方向卷积提取的点云空间结构特征；最后，利用全连接层进行三维人脸的分类，从而实现三维人脸识别。在CASIA数据库上的结果显示，所提方法的平均识别率为96.34%，优于PointNet、PointNet++、PointCNN和空间聚合网络（SAN）这几种分类网络。

3D face recognition based on hierarchical feature network

Focused on the problems of multiple expression variations, multiple pose variations as well as varying-degree missing face point cloud data in Three-Dimensional (3D) faces, 3D point cloud face data was exploratively applied to PointNet series classification networks, and the recognition results were compared and analyzed, then a new network framework named HFN (Hierarchical Feature Network) was proposed. First, the point cloud with fixed points was randomly sampled after data preprocessing. Second, the point fixed point cloud was input into SA (Set Abstraction) module in order to obtain the centroid points and neighborhood points of the local areas, and extract the features of the local areas, then the point cloud spatial structural features extracted from DSA (Directional Spatial Aggregation) module based on multi-directional convolution were mosaicked. Finally, the full connection layer was used to perform the classification of 3D faces, so as to realize the 3D face recognition. The results on CASIA database show that the average recognition rate of the proposed method is 96.34%, which is better than those of classification networks such as PointNet, PointNet++, PointCNN and Spatial Aggregation Net (SAN).