  Morphing attacks keep threatening biometric systems, especially face
recognition systems. Over time they have become simpler to perform and more
realistic, as such, the usage of deep learning systems to detect these attacks
has grown. At the same time, there is a constant concern regarding the lack of
interpretability of deep learning models. Balancing performance and
interpretability has been a difficult task for scientists. However, by
leveraging domain information and proving some constraints, we have been able
to develop IDistill, an interpretable method with state-of-the-art performance
that provides information on both the identity separation on morph samples and
their contribution to the final prediction. The domain information is learnt by
an autoencoder and distilled to a classifier system in order to teach it to
separate identity information. When compared to other methods in the literature
it outperforms them in three out of five databases and is competitive in the
remaining.
