  Face morphing attack detection is challenging and presents a concrete and
severe threat for face verification systems. Reliable detection mechanisms for
such attacks, which have been tested with a robust cross-database protocol and
unknown morphing tools still is a research challenge. This paper proposes a
framework following the Few-Shot-Learning approach that shares image
information based on the siamese network using triplet-semi-hard-loss to tackle
the morphing attack detection and boost the clustering classification process.
This network compares a bona fide or potentially morphed image with triplets of
morphing and bona fide face images. Our results show that this new network
cluster the data points, and assigns them to classes in order to obtain a lower
equal error rate in a cross-database scenario sharing only small image numbers
from an unknown database. Few-shot learning helps to boost the learning
process. Experimental results using a cross-datasets trained with FRGCv2 and
tested with FERET and the AMSL open-access databases reduced the BPCER10 from
43% to 4.91% using ResNet50 and 5.50% for MobileNetV2.
