  The vulnerability of facial recognition systems to face morphing attacks is
well known. Many different approaches for morphing attack detection have been
proposed in the scientific literature. However, the morphing attack detection
algorithms proposed so far have only been trained and tested on datasets whose
distributions of image characteristics are either very limited (e.g. only
created with a single morphing tool) or rather unrealistic (e.g. no print-scan
transformation). As a consequence, these methods easily overfit on certain
image types and the results presented cannot be expected to apply to real-world
scenarios. For example, the results of the latest NIST Face Recognition Vendor
Test MORPH show that the submitted MAD algorithms lack robustness and
performance when considering unseen and challenging datasets. In this work,
subsets of the FERET and FRGCv2 face databases are used to create a large
realistic database for training and testing of morphing attack detection
algorithms, containing a large number of ICAO-compliant bona fide facial
images, corresponding unconstrained probe images, and morphed images created
with four different tools. Furthermore, multiple post-processings are applied
on the reference images, e.g. print-scan and JPEG2000 compression. On this
database, previously proposed differential morphing algorithms are evaluated
and compared. In addition, the application of deep face representations for
differential morphing attack detection algorithms is investigated. It is shown
that algorithms based on deep face representations can achieve very high
detection performance (less than 3\%~\mbox{D-EER}) and robustness with respect
to various post-processings. Finally, the limitations of the developed methods
are analyzed.
