With the rise of deep learning, facial recognition technology has seen extensive research and rapid development. Although facial recognition is considered a mature technology, we find that existing open-source models and commercial algorithms lack robustness in certain real-world Out-of-Distribution (OOD) scenarios, raising concerns about the reliability of these systems. In this paper, we introduce OODFace, which explores the OOD challenges faced by facial recognition models from two perspectives: common corruptions and appearance variations. We systematically design 30 OOD scenarios across 9 major categories tailored for facial recognition. By simulating these challenges on public datasets, we establish three robustness benchmarks: LFW-C/V, CFP-FP-C/V, and YTF-C/V. We then conduct extensive experiments on 19 different facial recognition models and 3 commercial APIs, along with extended experiments on face masks, Vision-Language Models (VLMs), and defense strategies to assess their robustness. Based on the results, we draw several key insights, highlighting the vulnerability of facial recognition systems to OOD data and suggesting possible solutions. Additionally, we offer a unified toolkit that includes all corruption and variation types, easily extendable to other datasets. We hope that our benchmarks and findings can provide guidance for future improvements in facial recognition model robustness.

翻译：随着深度学习的兴起，人脸识别技术得到了广泛研究并迅速发展。尽管人脸识别被视为一项成熟技术，但我们发现现有的开源模型与商业算法在某些现实世界分布外（OOD）场景中缺乏鲁棒性，这引发了对此类系统可靠性的担忧。本文提出OODFace，从常见干扰和外观变化两个维度探究人脸识别模型面临的OOD挑战。我们针对人脸识别任务系统性地设计了涵盖9大类别、共计30种OOD场景。通过在公开数据集上模拟这些挑战，我们建立了三个鲁棒性基准测试集：LFW-C/V、CFP-FP-C/V与YTF-C/V。随后对19种不同人脸识别模型及3个商业API进行大量实验，并扩展至口罩场景、视觉语言模型（VLMs）及防御策略的评估以检验其鲁棒性。基于实验结果，我们总结出若干关键发现，揭示了人脸识别系统对OOD数据的脆弱性，并提出可能的解决方案。此外，我们提供包含所有干扰与变化类型的统一工具包，可便捷扩展至其他数据集。我们期望本研究的基准测试集与发现能为未来提升人脸识别模型鲁棒性提供指引。