Abstract
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.
Abstract (translated)
面部生物识别是确保智能手机可靠且值得信赖的认证的重要组成部分。然而，面部生物识别系统容易受到展示攻击（PAs）的影响，而且比例如3D硅胶面部口罩等更复杂的展示攻击工具将使攻击者轻松欺骗面部识别系统。在这项工作中，我们提出了一种基于智能手机前摄像头捕获的3D点云的新型展示攻击检测（PAD）算法来检测展示攻击。所提出的PAD算法，VoxAtnNet，对3D点云进行处理以实现体素化以保留空间结构。然后，使用新颖的卷积注意网络对体素化的3D样本进行训练，以检测智能手机上的PAs。在构建了包含真实和两种不同3D PPI（3D硅胶面部口罩和贴纸照片面具）的新建3D面部点云数据集上进行了大量实验，结果产生了3480个样本。将所提出的方法与现有方法进行比较，以通过三种不同的评估协议 benchmark检测性能。实验结果表明，与已知和未知面部展示攻击相比，所提出的方法在检测方面都取得了显著改进。
URL
https://arxiv.org/abs/2404.12680
