  Morphing attacks are one of the many threats that are constantly affecting
deep face recognition systems. It consists of selecting two faces from
different individuals and fusing them into a final image that contains the
identity information of both. In this work, we propose a novel regularisation
term that takes into account the existent identity information in both and
promotes the creation of two orthogonal latent vectors. We evaluate our
proposed method (OrthoMAD) in five different types of morphing in the FRLL
dataset and evaluate the performance of our model when trained on five distinct
datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art
results in the majority of the experiments, and competitive results in the
others. The code of this paper will be publicly available.
