  Face morphing represents nowadays a big security threat in the context of
electronic identity documents as well as an interesting challenge for
researchers in the field of face recognition. Despite of the good performance
obtained by state-of-the-art approaches on digital images, no satisfactory
solutions have been identified so far to deal with cross-database testing and
printed-scanned images (typically used in many countries for document issuing).
In this work, novel approaches are proposed to train Deep Neural Networks for
morphing detection: in particular generation of simulated printed-scanned
images together with other data augmentation strategies and pre-training on
large face recognition datasets, allowed to reach state-of-the-art accuracy on
challenging datasets from heterogeneous image sources.
