VoxAtnNet：一种用于泛化人脸呈现攻击检测的3D点云卷积神经网络
VoxAtnNet: A 3D Point Clouds Convolutional Neural Network for Generalizable Face Presentation Attack Detection
AI总结：
针对智能手机人脸识别易受3D面具等呈现攻击的问题，提出基于3D点云体素化与卷积注意力网络的PAD算法VoxAtnNet，在3480样本数据集上经三种协议验证，对已知和未知攻击检测性能均有提升。
摘要：
面部生物识别是智能手机确保可靠和可信认证的重要组成部分。然而，人脸生物识别系统容易受到呈现攻击（PAs）的影响，并且更复杂的呈现攻击工具（如3D硅胶面具）的出现将使攻击者轻易欺骗人脸识别系统。在这项工作中，我们提出了一种基于智能手机前置摄像头捕获的3D点云的新型呈现攻击检测（PAD）算法，用于检测呈现攻击。所提出的PAD算法VoxAtnNet处理3D点云以获得体素化，以保留空间结构。然后，使用新颖的卷积注意力网络训练体素化的3D样本，以在智能手机上检测PAs。我们在新构建的3D人脸点云数据集上进行了大量实验，该数据集包含真实人脸和两种不同的3D呈现攻击工具（3D硅胶面具和包裹照片面具），共产生3480个样本。将所提出方法的性能与现有方法进行比较，使用三种不同的评估协议来基准检测性能。实验结果表明，所提出的方法在检测已知和未知人脸呈现攻击方面均表现出改进的性能。
英文摘要：
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.
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