  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.
