This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard experimental protocols. FRCSyn-onGoing is based on the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first face recognition international challenge aiming to explore the use of real and synthetic data independently, and also their fusion, in order to address existing limitations in the technology. Specifically, FRCSyn-onGoing targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. To enhance face recognition performance, FRCSyn-onGoing strongly advocates for information fusion at various levels, starting from the input data, where a mix of real and synthetic domains is proposed for specific tasks of the challenge. Additionally, participating teams are allowed to fuse diverse networks within their proposed systems to improve the performance. In this article, we provide a comprehensive evaluation of the face recognition systems and results achieved so far in FRCSyn-onGoing. The results obtained in FRCSyn-onGoing, together with the proposed public ongoing benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

中文翻译：

---

FRCSyn-onGoing：对真实数据和合成数据进行基准测试和综合评估，以改进人脸识别系统

本文介绍了 FRCSyn-onGoing，这是一项针对人脸识别的持续挑战，研究人员可以使用大型公共数据库和标准实验协议，在开放式通用平台上轻松地将其系统与最先进的系统进行基准测试。 FRCSyn-onGoing 以 WACV 2024 上组织的合成数据时代的人脸识别挑战赛 (FRCSyn) 为基础。这是第一个人脸识别国际挑战赛，旨在探索真实数据和合成数据的独立使用以及它们的融合。以解决该技术现有的局限性。具体来说，FRCSyn-onGoing 针对的是与数据隐私问题、人口统计偏差、对未见过场景的泛化以及挑战性场景中的性能限制相关的问题，包括注册和测试之间的显着年龄差异、姿势变化和遮挡。为了提高人脸识别性能，FRCSyn-onGoing 强烈主张从输入数据开始进行各个级别的信息融合，针对挑战的特定任务提出真实域和合成域的混合。此外，参与团队可以在其提议的系统中融合不同的网络以提高性能。在本文中，我们对 FRCSyn-onGoing 中的人脸识别系统和迄今为止取得的成果进行了全面评估。 FRCSyn-onGoing 中获得的结果以及拟议的公共持续基准，对合成数据的应用以改进人脸识别技术做出了重大贡献。