  Face Recognition systems (FRS) have been found to be vulnerable to morphing
attacks, where the morphed face image is generated by blending the face images
from contributory data subjects. This work presents a novel direction for
generating face-morphing attacks in 3D. To this extent, we introduced a novel
approach based on blending 3D face point clouds corresponding to contributory
data subjects. The proposed method generates 3D face morphing by projecting the
input 3D face point clouds onto depth maps and 2D color images, followed by
image blending and wrapping operations performed independently on the color
images and depth maps. We then back-projected the 2D morphing color map and the
depth map to the point cloud using the canonical (fixed) view. Given that the
generated 3D face morphing models will result in holes owing to a single
canonical view, we have proposed a new algorithm for hole filling that will
result in a high-quality 3D face morphing model. Extensive experiments were
conducted on the newly generated 3D face dataset comprising 675 3D scans
corresponding to 41 unique data subjects and a publicly available database
(Facescape) with 100 data subjects. Experiments were performed to benchmark the
vulnerability of the {proposed 3D morph-generation scheme against} automatic
2D, 3D FRS, and human observer analysis. We also presented a quantitative
assessment of the quality of the generated 3D face-morphing models using eight
different quality metrics. Finally, we propose three different 3D face Morphing
Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face
morphing attack detection techniques.
