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Morphing attacks are one of the many threats that are constantly affectingdeep face recognition systems. It consists of selecting two faces fromdifferent individuals and fusing them into a final image that contains theidentity information of both. In this work, we propose a novel regularisationterm that takes into account the existent identity information in both andpromotes the creation of two orthogonal latent vectors. We evaluate ourproposed method (OrthoMAD) in five different types of morphing in the FRLLdataset and evaluate the performance of our model when trained on five distinctdatasets. With a small ResNet-18 as the backbone, we achieve state-of-the-artresults in the majority of the experiments, and competitive results in theothers. The code of this paper will be publicly available.

OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement