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

Authors :
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
Publication Year :
2023

Abstract

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.<br />Comment: Accepted at IJCB2 023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2311.05336
Document Type :
Working Paper