Huber, Marco ; Boutros, Fadi ; Luu, Anh Thi ; Raja, Kiran ; Ramachandra, Raghavendra ; Damer, Naser ; Neto, Pedro C. ; Goncalves, Tiago ; Sequeira, Ana F. ; Cardoso, Jaime S. ; Tremoco, Joao ; Lourenco, Miguel ; Serra, Sergio ; Cermeno, Eduardo ; Ivanovska, Marija ; Batagelj, Borut ; Kronovsek, Andrej ; Peer, Peter ; Struc, Vitomir (2022)
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data.
International Joint Conference on Biometrics (IJCB). Abu Dhabi, UAE (10.10.2022-13.10.2022)
doi: 10.1109/IJCB54206.2022.10007950
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people’s privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://github.com/marcohuber/SYN-MAD-2022.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Huber, Marco ; Boutros, Fadi ; Luu, Anh Thi ; Raja, Kiran ; Ramachandra, Raghavendra ; Damer, Naser ; Neto, Pedro C. ; Goncalves, Tiago ; Sequeira, Ana F. ; Cardoso, Jaime S. ; Tremoco, Joao ; Lourenco, Miguel ; Serra, Sergio ; Cermeno, Eduardo ; Ivanovska, Marija ; Batagelj, Borut ; Kronovsek, Andrej ; Peer, Peter ; Struc, Vitomir |
| Art des Eintrags: | Bibliographie |
| Titel: | SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | IEEE |
| Buchtitel: | 2022 IEEE International Joint Conference on Biometrics |
| Veranstaltungstitel: | International Joint Conference on Biometrics (IJCB) |
| Veranstaltungsort: | Abu Dhabi, UAE |
| Veranstaltungsdatum: | 10.10.2022-13.10.2022 |
| DOI: | 10.1109/IJCB54206.2022.10007950 |
| Kurzbeschreibung (Abstract): | This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people’s privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://github.com/marcohuber/SYN-MAD-2022. |
| Freie Schlagworte: | Biometrics, Deep learning, Face recognition, Research challenges, Morphing attack |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 06 Mär 2023 09:37 |
| Letzte Änderung: | 27 Jul 2023 14:32 |
| PPN: | 509987400 |
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