# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Masked Face Recognition Approaches
#### 5.3.3 Deep Learning-Based Masked Face Approaches
The refined ResNet-50 architecture acted as the backbone for feature extraction, augmented with CBAM to enhance efficiency in feature extraction. The resulting 512-dimensional face embeddings were optimized using the ArcFace loss function, leading to significant enhancements in MFR performance. Experimental findings corroborated the effectiveness of the proposed approach, underscoring its potential for practical applications within the realm of COVID-19 safety protocols. [PERSON] et al. [107] presented a real-time masked detection service and mobile face recognition application aimed at identifying individuals who either do not wear masks or wear them incorrectly. Through the utilization of fine-tuned lightweight convolutional neural networks (CNNs), the system achieved a validation accuracy of 90.40% using face samples from 12 individuals. The proposed approach adopted a two-stage methodology: initially, a deep model discerns the mask status, categorizing individuals as masked, unmasked, or improperly masked. Subsequently, a face identification module employs traditional and eye-based recognition techniques to identify individuals. This system represents a significant advancement in masked face recognition, effectively addressing the challenges associated with masks in digital environments. [PERSON] et al. [45] introduced MFCosface, an innovative algorithm tailored for masked face recognition amid the challenges posed by the COVID-19 pandemic. To mitigate the shortage of masked face data, the algorithm incorporated a novel masked face image generation method that utilizes key facial features for realistic image synthesis. Departing from conventional triplet loss approaches, MFCosface employed a large margin cosine loss function, optimizing feature mapping to bolster inter-class discrimination. Moreover, an Att-inception module was introduced to prioritize unoccluded facial regions, essential for precise recognition. Experimental findings across diverse datasets underscored the algorithm's notable enhancement in masked face recognition accuracy, presenting a promising solution for facial recognition in mask-wearing scenarios. [PERSON] et al. [109] proposed a lightweight deep learning approach, leveraging the HSTU Masked Face Dataset (HMFD) and employing a customized CNN model to improve masked face identification. Integration of key techniques such as dropout, batch normalization, and depth-wise normalization optimized the model performance while minimizing complexity. In comparison to established deep learning models like VGG16 and MobileNet, the proposed model achieved a superior recognition accuracy of 97%. The methodology encompasses dataset preprocessing, model creation, training, testing, and evaluation, ensuring a robust performance in real-world scenarios. Transfer learning from pre-trained models such as VGG16 and VGG19, along with grid search for hyperparameter optimization, enhanced the model effectiveness. The architecture incorporated depthwise separable convolutions and carefully chosen layers to strike a balance between computational efficiency and accuracy, demonstrating exceptional performance even when facial features were partially obscured by masks. With an emphasis on simplicity and effectiveness, this lightweight CNN model offers a promising solution for recognizing masked faces, contributing to public health and safety efforts during the pandemic. [PERSON] et al. [109] conducted a study aimed at improving facial and masked facial recognition using deep learning and machine learning methods. Unlike previous research that often overlooked parameter optimization, this study employed a sophisticated approach. By integrating grid search, hyperparameter tuning, and nested cross-validation, significant progress was achieved. The SVM model, after hyperparameter tuning, achieved the highest accuracy of 99.912%. Real-world testing confirmed the efficacy of the approach in accurately identifying individuals wearing masks.