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%.

提出了一种从两个真实表达较为中性的 3D 面点云中生成人脸变形的新方法，通过将几何和颜色信息进行平均来生成面部变形点云，通过大量的漏洞性实验证明了该方法的有效性，达到了 97.93% 的广义变形攻击潜力，优于现有的 81.61% 的最先进技术 (State-of-the-art)。