## 2 Literature Review
In addition, they created a mask-aware dynamic model based on deep learning that can distinguish faces in the presence and absence of facial masks. A real-world masked face recognition dataset was used in the evaluation. LDOP-based descriptors achieved a maximum accuracy of 99.60% in facial mask detection. In the presence of a facial mask, their proposed dynamic ensemble model has 99.53% accuracy. In [17], a hybrid face mask detection model was proposed that combined deep and traditional machine learning. There were two phases to the proposed framework. The first component was created to extract features using Resnet 50. The second component was created to help with the classification of face masks utilizing SVM, decision trees, and an ensemble approach. The investigation focused on three face-masked datasets. The Simulated Masked Face Dataset (SMFD), Labeled Faces in the Wild (LFW), and RMFD are the three datasets that were used, and accuracies of 99.49%, 100%, and 99.64%, respectively, were achieved on the test datasets. The authors proposed a complete training pipeline based on the loss function [18] and ArcFace model [19], with numerous changes to the backbone and loss function. They used the ResNet-50 model as a backbone. For MS1 MV2, they achieved a mask-usage detection accuracy of 99.78%. They presented experimental results for 10 different face recognition benchmarks. Their findings showed that their strategy regularly exceeded the state of the art in extensive tests. The COVID-19 outbreak led to masked face recognition (MFR) development [20], but overemphasizing it harms standard face recognition. MFR should be treated as a mask bias, not a separate task. The study examined how face masks influenced emotion recognition in first- and fifth-graders, along with young adults [21], considering mask presence, color, and emotion type. The results showed masks affected recognizing fear and sadness, but not anger or neutrality. This study [22] aims to create an attendance system using face recognition and mask detection, accessible online via a browser interface. No special software installation is needed; users can access it through any terminal. The system records attendance data centrally in an online database, utilizing biometric face signatures. Users' profiles are loaded with face-image samples. Initial steps involve SVM-based model training for face recognition and synthetic data for identifying masked users. The goal is an efficient system for attendance management, even with face masks. In response to widespread mask-wearing during COVID-19 [23], conventional face recognition struggles. This article proposes an eyebrow-focused network for masked face recognition, using local features like eyebrows due to limited visible cues. The approach includes feature extraction, eyebrow pooling, and fusion using a graph convolutional network. Tested on real-world and synthetic datasets, the method outperforms existing techniques, effectively addressing masked face recognition challenges. DeepMasknet [24] was introduced to deal with mask-wearing issue. They also created a new diverse dataset, MDMFR, for evaluation. DeepMasknet outperforms existing models across datasets, providing a solution for COVID-19 challenges. COVID-19 challenges traditional face recognition due to increased mask-wearing [25]. Limited facial data hampers recognition, prompting experiments with CNN architectures and altered methods. The study evaluates existing CNN-based systems using entirely masked-face datasets, showing the importance of network depth and suggesting adjusted parameters. Empirical analysis guides new parameter values for masked face recognition. Another paper introducing a method to improve face recognition with masks [26] employs mask transfer for data augmentation and presents Attention-Aware Masked Face Recognition (AMaskNet) consisting of a feature extractor and a contribution estimator. Amid COVID-19, mandatory mask use prompted the development [27] of a system recognizing people wearing masks from photos.