对比不同主干网络在戴口罩人脸识别中的表现，指导实际部署
Masked Face Recognition under Different Backbones
- 系统测试r100、r50、vit等主干网络在有无口罩场景下的性能
- r100_mask_v2在戴口罩时达90.07%准确率，优于r50系列
- vit-small/tiny在遮挡场景下表现突出，适合高遮挡环境
后疫情时代，民航安检中大量乘客佩戴口罩，给传统人脸识别模型带来挑战。主干网络是人脸识别模型的核心组件。标准测试中，r100系列表现优异（0.01%误报率下准确率超98%，搜索任务中top1/top5成绩高），r50次之，r34_mask_v1表现落后。在戴口罩测试中，r100_mask_v2领先（准确率90.07%），r50_mask_v3在r50系列中最佳但仍逊于r100。vit-small/tiny在遮挡场景下表现强劲，有效性提升显著。通过大量对比实验，本文全面评估多个核心主干网络在有无口罩情况下的表现，揭示模型差异对识别效果的影响，并提供具体部署建议。
原文摘要 · Abstract (English)
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.
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