SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Huber, M (Author) Other; Boutros, F (Author) Other; Luu, AT (Author) Other; Raja, K (Author) Other; Ramachandra, R (Author) Other; Damer, N (Author) Other; Neto, PC (Author) Other; Goncalves, T (Author) Other; Sequeira, AF (Author) Other; Jaime S Cardoso (Author) FEUP; Tremoco, J (Author) Other; Lourenco, M (Author) Other; Serra, S (Author) Other; Cermeno, E (Author) Other; Ivanovska, M (Author) Other; Batagelj, B (Author) Other; Kronovsek, A (Author) Other; Peer, P (Author) Other; Struc, V (Author) Other

Conference proceedings International

Title: 2022 IEEE INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB)

Pages: 1-10

IEEE International Joint Conference on Biometrics (IJCB)

Abu Dhabi, U ARAB EMIRATES, OCT 10-13, 2022

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-00X-6A1

DOI: 10.1109/ijcb54206.2022.10007950

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10