SDFR: Synthetic Data for Face Recognition competition

Abstract

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMLarge-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussedThe organization of this competition was supported by the H2020 TReSPAsS-ETN Marie Skłodowska-Curie early training network (grant agreement 860813) as well as the Hasler foundation through the “Responsible Face Recognition” (SAFER) project. The work of BioLab team received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 883356. The submitted solution by the IGD-IDiff-Face team has been funded by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research, Science and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE. The work of BiDA-PRA team was supported by INTER-ACTION (PID2021-1265210B-I00 MICINN/FEDER), Cátedra ENIA UAM-VERIDAS en IA Responsable (NextGenerationEU PRTR TSI-100927-2023-2), and R&D Agreement DGGC/UAM/FUAM for Biometrics and Cybersecurity. The work of BiDA-PRA team was also supported by the European Union - Next Generation EU through the Italian Ministry of University and Research (MUR) within the PRIN PNRR 2022 - BullyBuster 2 - the ongoing fight against bullying and cyberbullying with the help of artificial intelligence for the human wellbeing (CUP: F53D23009240001