Face anti-spoofing plays a crucial role in protecting face recognition
systems from various attacks. Previous model-based and deep learning approaches
achieve satisfactory performance for 2D face spoofs, but remain limited for
more advanced 3D attacks such as vivid masks. In this paper, we address 3D face
anti-spoofing via the proposed Hypergraph Convolutional Neural Networks
(HGCNN). Firstly, we construct a computation-efficient and posture-invariant
face representation with only a few key points on hypergraphs. The hypergraph
representation is then fed into the designed HGCNN with hypergraph convolution
for feature extraction, while the depth auxiliary is also exploited for 3D mask
anti-spoofing. Further, we build a 3D face attack database with color, depth
and infrared light information to overcome the deficiency of 3D face
anti-spoofing data. Experiments show that our method achieves the
state-of-the-art performance over widely used 3D and 2D databases as well as
the proposed one under various tests.

本文提出一种基于 Hypergraph 卷积神经网络 (HGCNN) 的 3D 人脸防假攻击方法，该方法利用构建的超图表示进行特征提取，在深度辅助下实现 3D 假面防护，并构建 3D 人脸攻击数据库进行验证，实验表明该方法在各类测评中均达到了最新的防御水平。