Facial biometrics are an essential components of smartphones to ensure
reliable and trustworthy authentication. However, face biometric systems are
vulnerable to Presentation Attacks (PAs), and the availability of more
sophisticated presentation attack instruments such as 3D silicone face masks
will allow attackers to deceive face recognition systems easily. In this work,
we propose a novel Presentation Attack Detection (PAD) algorithm based on 3D
point clouds captured using the frontal camera of a smartphone to detect
presentation attacks. The proposed PAD algorithm, VoxAtnNet, processes 3D point
clouds to obtain voxelization to preserve the spatial structure. Then, the
voxelized 3D samples were trained using the novel convolutional attention
network to detect PAs on the smartphone. Extensive experiments were carried out
on the newly constructed 3D face point cloud dataset comprising bona fide and
two different 3D PAIs (3D silicone face mask and wrap photo mask), resulting in
3480 samples. The performance of the proposed method was compared with existing
methods to benchmark the detection performance using three different evaluation
protocols. The experimental results demonstrate the improved performance of the
proposed method in detecting both known and unknown face presentation attacks.

提出了一种基于 3D 点云的新型演示攻击检测算法 VoxAtnNet，用于侦测手机上的面部演示攻击。通过处理 3D 点云获取体素化的方式，以保留空间结构，然后使用新型卷积注意力网络对体素化的 3D 样本进行训练，实验结果表明该方法在检测已知和未知面部演示攻击的性能方面有所提升。