3D Face Morphing Attack Generation using Non-Rigid Registration

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%.