Abstract
Face Recognition Systems (FRS) are widely used in commercial environments, such as e-commerce and e-banking, owing to their high accuracy in real-world conditions. However, these systems are vulnerable to facial morphing attacks, which are generated by blending face color images of different subjects. This paper presents a new method for generating 3D face morphs from two bona fide point clouds. The proposed method first selects bona fide point clouds with neutral expressions. The two input point clouds were then registered using a Bayesian Coherent Point Drift (BCPD) without optimization, and the geometry and color of the registered point clouds were averaged to generate a face morphing point cloud. The proposed method generates 388 face-morphing point clouds from 200 bona fide subjects. The effectiveness of the method was demonstrated through extensive vulnerability experiments, achieving a Generalized Morphing Attack Potential (G-MAP) of 97.93%, which is superior to the existing state-of-the-art (SOTA) with a G-MAP of 81.61%.
Abstract (translated)
面部识别系统(FRS)在商业环境中(如电子商务和电子银行)得到了广泛应用，因为它们在现实情况下的准确度高。然而，这些系统容易受到由不同主题混合生成面部颜色图像的变形攻击。本文提出了一种从两个真实点云生成3D面部变形的方法。与优化无关，两个输入点云使用贝叶斯一致性点漂移(BCPD)进行注册，然后平均几何和颜色生成面部变形点云。该方法从200个真实主题中生成388个面部变形点云。通过广泛的漏洞实验，该方法的有效性得到了证明，实现了97.93%的泛化形态攻击潜力(G-MAP)，远高于现有状态下的81.61%。
URL
https://arxiv.org/abs/2404.15765
