Neto, Pedro C. ; Goncalves, Tiago ; Huber, Marco ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S. (2022)
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement.
21st International Conference of the Biometrics Special Interest Group (BIOSIG'22). Darmstadt, Germany (14.09.2022-16.09.2022)
doi: 10.1109/BIOSIG55365.2022.9897057
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Neto, Pedro C. ; Goncalves, Tiago ; Huber, Marco ; Damer, Naser ; Sequeira, Ana F. ; Cardoso, Jaime S. |
| Art des Eintrags: | Bibliographie |
| Titel: | OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement |
| Sprache: | Englisch |
| Publikationsjahr: | 27 September 2022 |
| Verlag: | Gesellschaft für Informatik e.V. |
| Buchtitel: | BIOSIG 2022: Proceedings of the 21st International Conference of the Biometrics Special Interest Group |
| Reihe: | Lecture Notes in Informatics |
| Band einer Reihe: | 329 |
| Veranstaltungstitel: | 21st International Conference of the Biometrics Special Interest Group (BIOSIG'22) |
| Veranstaltungsort: | Darmstadt, Germany |
| Veranstaltungsdatum: | 14.09.2022-16.09.2022 |
| DOI: | 10.1109/BIOSIG55365.2022.9897057 |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | Biometrics, Face recognition, Morphing attack, Deep learning, Machine learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 23 Nov 2022 08:21 |
| Letzte Änderung: | 28 Mär 2023 11:58 |
| PPN: | 506362469 |
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