RP-Net: A PointNet++ 3D Face Recognition Algorithm Integrating RoPS Local Descriptor

As a biometric identification method in the post-epidemic era, face recognition owing more and more attention in practical applications to its non-contact and interaction-friendly advantages. Researchers more favor 3D faces because they have richer spatial information than 2D faces and are not easily affected by the environment. However 3D faces are not all collected in normal environments. To enhance the facial features of 3D faces and improve the recognition degree of 3D faces in weak-light or dark environments, a 3D face recognition algorithm based on point cloud depth learning is proposed. First, 3D faces are automatically detected from 3D raw data and preprocessed, including nose-tip detection and face cropping, spike removal and hole filling, and surface normals. Then, rotated projection statistical local feature descriptors (RoPS) are integrated into the PointNet++ network to describe and classify local features. Finally, feature matching is performed using the nearest neighbor distance ratio. The algorithm was tested on the Bosphorus and CASIA-3D datasets, and good results were obtained in a simulated weak-light environment.