  Point clouds-based Networks have achieved great attention in 3D object
classification, segmentation and indoor scene semantic parsing. In terms of
face recognition, 3D face recognition method which directly consume point
clouds as input is still under study. Two main factors account for this: One is
how to get discriminative face representations from 3D point clouds using deep
network; the other is the lack of large 3D training dataset. To address these
problems, a data-free 3D face recognition method is proposed only using
synthesized unreal data from statistical 3D Morphable Model to train a deep
point cloud network. To ease the inconsistent distribution between model data
and real faces, different point sampling methods are used in train and test
phase. In this paper, we propose a curvature-aware point sampling(CPS) strategy
replacing the original furthest point sampling(FPS) to hierarchically
down-sample feature-sensitive points which are crucial to pass and aggregate
features deeply. A PointNet++ like Network is used to extract face features
directly from point clouds. The experimental results show that the network
trained on generated data generalizes well for real 3D faces. Fine tuning on a
small part of FRGCv2.0 and Bosphorus, which include real faces in different
poses and expressions, further improves recognition accuracy.
