Publikationen

Autor	Neto, Pedro C.; Goncalves, Tiago; Huber, Marco; Damer, Naser; Sequeira, Ana F.; Cardoso, Jaime S.
Datum	2022
Art	Conference Paper
Abstrakt	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.
Konferenz	Gesellschaft für Informatik, Special Interest Group on Biometrics (BIOSIG International Conference) 2022
ISBN	978-3-88579-723-4
Publisher	Gesellschaft für Informatik
Projekt	Next Generation Biometric Systems
Url	https://publica.fraunhofer.de/handle/publica/428120

OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement