Face Recognition Systems (FRS) are vulnerable to various attacks performed
directly and indirectly. Among these attacks, face morphing attacks are highly
potential in deceiving automatic FRS and human observers and indicate a severe
security threat, especially in the border control scenario. This work presents
a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic
Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD)
algorithm based on the spherical interpolation and hierarchical fusion of deep
features computed from six different pre-trained deep Convolutional Neural
Networks (CNNs). Extensive experiments are carried out on the newly generated
face morphing dataset (SCFace-Morph) based on the publicly available SCFace
dataset by considering the real-life scenario of Automatic Border Control (ABC)
gates. Experimental protocols are designed to benchmark the proposed and
state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and
capture distances. Obtained results have indicated the superior performance of
the proposed D-MAD method compared to the existing methods.Comment: The paper is accepted at the International Joint Conference on
Biometrics (IJCB) 202