  Face morphing attack detection is emerging as an increasingly challenging
problem owing to advancements in high-quality and realistic morphing attack
generation. Reliable detection of morphing attacks is essential because these
attacks are targeted for border control applications. This paper presents a
multispectral framework for differential morphing-attack detection (D-MAD). The
D-MAD methods are based on using two facial images that are captured from the
ePassport (also called the reference image) and the trusted device (for
example, Automatic Border Control (ABC) gates) to detect whether the face image
presented in ePassport is morphed. The proposed multispectral D-MAD framework
introduce a multispectral image captured as a trusted capture to acquire seven
different spectral bands to detect morphing attacks. Extensive experiments were
conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143
unique data subjects that were captured using both visible and multispectral
cameras in multiple sessions. The results indicate the superior performance of
the proposed multispectral framework compared to visible images.
