Masked Face Recognition under Different Backbones

2025-10-22

2025 IEEE 7th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

AI Summary

This study addresses the significant challenge posed by widespread mask-wearing in post-pandemic civil aviation security screening to conventional face recognition systems. It presents the first systematic evaluation of mainstream convolutional neural networks (ResNet-34/50/100 and their mask-optimized variants) and Vision Transformers (ViT-Small/Tiny) under occluded conditions, leveraging large-scale face verification and retrieval experiments to elucidate the critical influence of model architecture on mask robustness. Results demonstrate that the r100_mask_v2 variant achieves 90.07% accuracy on masked-face identification, while ViT models also exhibit strong robustness, collectively offering clear guidance for model selection and optimization in real-world deployment scenarios.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Masked Face Recognition
• Backbone Networks
• Face Recognition
• Post-pandemic Era
• Civil Aviation Security

Innovation

• masked face recognition
• backbone networks
• model robustness
• vision transformer
• comparative evaluation