Erratum to the paper (Zhang et al., 2025): corrections to Table IV and the data in Page 3, Section A. In the post-pandemic era, a high proportion of civil aviation passengers wear masks during security checks, posing significant challenges to traditional face recognition models. The backbone network serves as the core component of face recognition models. In standard tests, r100 series models excelled (98%+ accuracy at 0.01% FAR in face comparison, high top1/top5 in search). r50 ranked second, r34_mask_v1 lagged. In masked tests, r100_mask_v2 led (90.07% accuracy), r50_mask_v3 performed best among r50 but trailed r100. Vit-Small/Tiny showed strong masked performance with gains in effectiveness. Through extensive comparative experiments, this paper conducts a comprehensive evaluation of several core backbone networks, aiming to reveal the impacts of different models on face recognition with and without masks, and provide specific deployment recommendations.

翻译：对论文（Zhang等人，2025年）的勘误：对表IV及第3页A节数据的更正。在后疫情时代，民航安检中佩戴口罩的旅客比例很高，这对传统人脸识别模型构成了重大挑战。骨干网络是人脸识别模型的核心组件。在标准测试中，r100系列模型表现出色（在人脸比对中，在0.01% FAR下准确率超过98%，在检索中top1/top5准确率高）。r50排名第二，r34_mask_v1落后。在口罩测试中，r100_mask_v2领先（准确率90.07%），r50_mask_v3在r50系列中表现最佳但仍落后于r100。Vit-Small/Tiny在口罩场景下表现出色，有效性有所提升。通过大量对比实验，本文对几种核心骨干网络进行了全面评估，旨在揭示不同模型在有/无口罩情况下对人脸识别的影响，并提供具体的部署建议。