High-precision 3D Facial Landmark Detection with Curvature-fused Graph Attention Network

With the rapid advancement of deep learning, 2D facial landmark detection algorithms have achieved satisfying results. However, due to the absence of large-scale accurately annotated 3D facial datasets, most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-real digital 3D faces. The performance of these algorithms is limited by the accuracy of 2D texture mapping onto 3D faces and the difference between digital faces and actual faces. To tackle these challenges, we have built a large-scale, high-precision 3D facial database using a structured light system. Facial landmarks within the database are marked multiple times to ensure accuracy. Building upon this foundation, we proposed a novel point cloud sampling method and 3D facial landmark detection algorithm. This method utilizes a curvature-fused graph attention network (CGAT) to directly predict landmark coordinates from 3D point clouds. Initially, we extracted a subsampled point set carrying curvature information from the original 3D facial point cloud via geometric point sampling (GPS). Then, we used curvature-encoded positional information as the learning component of the attention module and employed it as a feature extractor to construct the CGAT. We assessed the performance of CGAT on two datasets, BU-3DFE and CIE-H3DF (Ours). Compared to the existing facial landmark detection algorithms, CGAT achieves higher accuracy.