  Large-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 discussed.
