Inclusive Review on Advances in Masked Human Face Recognition Technologies

TL;DR

This paper reviews recent advances in masked human face recognition, emphasizing deep learning techniques, challenges, solutions, and future research directions to enhance system robustness and applicability.

Contribution

It provides a comprehensive overview of recent developments, challenges, and technological solutions in masked face recognition, highlighting deep learning methods and future trends.

Findings

Deep learning techniques significantly improve masked face recognition accuracy.

Data augmentation and multimedia methods help overcome occlusion challenges.

Advances in network design enhance system robustness in real-world scenarios.

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.