MASK-MORPH: Does Morphing of Custom 3D Face Masks Threatens the Face Recognition Systems?

Abstract

Face Recognition Systems (FRS) are vulnerable to morphing attacks that are targeted towards highly secured applications, including Automatic Border Control (ABC) gates. In this paper, we investigate a 3D-face custom silicone mask as the source for generating face morphing attacks for the first time. We present a systematic study to benchmark the attack potential of mask morphing (digital) attacks on both commercial and academic FRS. To this extent, a new dataset is constructed using eight custom 3D silicone face masks and corresponding bona fide face images captured using three different smartphones. The mask morphing is carried out using a landmark-based method, and the newly constructed dataset comprises 635 bona fide, 1034 face masks and 613 mask morphing face images. Extensive experiments are carried out to benchmark the attack potential and detection of mask morphing attacks on FRS.