Abstract
Masked Face Recognition (MFR) is an increasingly important area in biometric recognition technologies, especially with the widespread use of masks as a result of the COVID-19 pandemic. This development has created new challenges for facial recognition systems due to the partial concealment of basic facial features. This paper aims to provide a comprehensive review of the latest developments in the field, with a focus on deep learning techniques, especially convolutional neural networks (CNNs) and twin networks (Siamese networks), which have played a pivotal role in improving the accuracy of covering face recognition. The paper discusses the most prominent challenges, which include changes in lighting, different facial positions, partial concealment, and the impact of mask types on the performance of systems. It also reviews advanced technologies developed to overcome these challenges, including data enhancement using artificial databases and multimedia methods to improve the ability of systems to generalize. In addition, the paper highlights advance in deep network design, feature extraction techniques, evaluation criteria, and data sets used in this area. Moreover, it reviews the various applications of masked face recognition in the fields of security and medicine, highlighting the growing importance of these systems in light of recurrent health crises and increasing security threats. Finally, the paper focuses on future research trends such as developing more efficient algorithms and integrating multimedia technologies to improve the performance of recognition systems in real-world environments and expand their applications.
Abstract (translated)
口罩面部识别（MFR）是生物识别技术领域中日益重要的一个分支，尤其是在新冠疫情导致广泛使用口罩的情况下。这一发展为面部识别系统带来了新的挑战，因为基本的面部特征部分被遮盖。本文旨在全面回顾该领域的最新进展，重点关注深度学习技术，尤其是卷积神经网络（CNNs）和孪生网络（Siamese networks），这些技术在提高覆盖脸孔识别精度方面发挥了关键作用。 论文讨论了最突出的挑战，包括光照变化、不同面部位置、部分遮挡以及口罩类型对系统性能的影响。此外，还回顾了为克服这些挑战而开发的先进技术，例如使用人工数据库进行数据增强和多媒体方法以提升系统的泛化能力。同时，文章强调了深度网络设计的进步、特征提取技术、评估标准及在此领域使用的数据集。 此外，论文审查了口罩面部识别在安全和医疗领域的各种应用，并突出了这些系统在面对反复出现的健康危机和日益严峻的安全威胁时的重要性。 最后，该文聚焦于未来的研究趋势，包括开发更高效的算法以及整合多媒体技术以提升现实环境中识别系统的性能并扩展其应用范围。
URL
https://arxiv.org/abs/2508.00841
