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Face Recognition Systems (FRS) are vulnerable to various attacks performeddirectly and indirectly. Among these attacks, face morphing attacks are highlypotential in deceiving automatic FRS and human observers and indicate a severesecurity threat, especially in the border control scenario. This work presentsa face morphing attack detection, especially in the On-The-Fly (OTF) AutomaticBorder Control (ABC) scenario. We present a novel Differential-MAD (D-MAD)algorithm based on the spherical interpolation and hierarchical fusion of deepfeatures computed from six different pre-trained deep Convolutional NeuralNetworks (CNNs). Extensive experiments are carried out on the newly generatedface morphing dataset (SCFace-Morph) based on the publicly available SCFacedataset by considering the real-life scenario of Automatic Border Control (ABC)gates. Experimental protocols are designed to benchmark the proposed andstate-of-the-art (SOTA) D-MAD techniques for different camera resolutions andcapture distances. Obtained results have indicated the superior performance ofthe proposed D-MAD method compared to the existing methods.

Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance