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)。

基于非刚性配准的 3D 人脸形变攻击生成

3D Face Morphing Attack Generation using Non-Rigid Registration

In recent years face recognition systems have been brought to the mainstream
due to development in hardware and software. Consistent efforts are being made
to make them better and more secure. This has also brought developments in 3D
face recognition systems at a rapid pace. These 3DFR systems are expected to
overcome certain vulnerabilities of 2DFR systems. One such problem that the
domain of 2DFR systems face is face image morphing. A substantial amount of
research is being done for generation of high quality face morphs along with
detection of attacks from these morphs. Comparatively the understanding of
vulnerability of 3DFR systems against 3D face morphs is less. But at the same
time an expectation is set from 3DFR systems to be more robust against such
attacks. This paper attempts to research and gain more information on this
matter. The paper describes a couple of methods that can be used to generate 3D
face morphs. The face morphs that are generated using this method are then
compared to the contributing faces to obtain similarity scores. The highest
MMPMR is obtained around 40% with RMMR of 41.76% when 3DFRS are attacked with
look-a-like morphs.

该研究论文的主要内容是研究面部识别系统中的 3D 面部形态学攻击，并描述了生成 3D 面部形态学和检测攻击的方法。