Publikationen

Autor	Fang, Meiling; Huber, Marco; Fierrez, Julian; Ramachandra, Raghavendra; Damer, Naser; Alkhaddour, Alhasan; Kasantcev, Maksim; Pryadchenko, Vasiliy; Yang, Ziyuan; Huangfu, Huijie; Chen, Yingyu; Zhang, Yi; Pan, Yuchen; Jiang, Junjun; Liu, Xianming; Sun, Xianyun; Wang, Caiyong; Liu, Xingyu; Chang, Zhaohua; Zhao, Guangzhe; Tapia, Juan; Gonzalez-Soler, Lazaro; Aravena, Carlos; Schulz, Daniel
Datum	2023
Art	Conference Paper
Abstrakt	This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.
Konferenz	International Joint Conference on Biometrics 2023
Projekt	Next Generation Biometric Systems
Url	https://publica.fraunhofer.de/handle/publica/464242

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data