# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Face Mask Recognition Approaches
#### 5.1.4 Transfer Learning  
Transfer learning is a technique in deep learning where a model trained on one task is repurposed as the starting point for a model on a different but related task. This approach leverages pre-trained networks, such as InceptionV3, to improve learning efficiency and model performance, particularly when data is limited.  
[PERSON] et al. [64] propose an automated method for detecting individuals not wearing masks in public and crowded areas during the COVID-19 health crisis. They employ transfer learning with the pre-trained InceptionV3 model, fine-tuning it specifically for this task. Training is conducted on the Simulated Masked Face Dataset (SMFD), augmented with techniques like shearing, contrasting, flipping, and blurring, while [PERSON] et al. [65] introduce a novel method for detecting whether individuals are wearing face masks using artificial intelligence technologies. They utilize deep convolutional neural networks (CNNs) to extract features from facial images, which are then classified using machine learning algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN). Despite the limited dataset of 1376 images, the combination of SVM with the MobileNetV2 model achieved a high classification accuracy of 97.1%.  
[PERSON] et al. [66] propose a hybrid model combining deep learning and classical machine learning techniques to detect face masks, a vital task during the COVID-19 pandemic. The model employs ResNet50 for extracting features from images in the first component, and ses decision trees, Support Vector Machine (SVM), and an ensemble algorithm for classification in the second component. The model was tested using three datasets: Real-World Masked Face Recognition Dataset (RMFRD), Simulated Masked Face Dataset (SMFD), and Labeled Faces in the Wild (LFW). It achieved high testing accuracies, notably 99.64% on RMFRD, 99.49% on SMFD, and 100% on LFW. Also, [PERSON] et al. [31] outline a two-stage hybrid machine learning approach for detecting mask wearing in public spaces to reduce the spread of COVID-19. The first stage uses a pre-trained Faster R-CNN model combined with an InceptionV2 architecture to identify potential mask-wearing regions. The second stage employs a Broad Learning System (BLS) to verify these detections by differentiating actual mask wearing from background elements. The method, tested on a new dataset comprising 7804 images and 26,403 mask instances, demonstrates high accuracy, achieving97.32% in simple scenes and 91.13% in complex scenes. [PERSON] [PERSON] et al. [68] integrate transfer learning and deep learning techniques to enhance accuracy and performance. Firstly, the face mask detection component employs Efficient-YOLOv3 with EfficientNet as the backbone, using CIoU loss to improve the detection precision and reduce the computational load. Secondly, the classification component differentiates between \"qualified\" masks (e.g., N95, disposable medical) and \"unqualified\" masks (e.g., cotton, sponge masks) using MobileNet to overcome the challenges associated with small datasets and overfitting.  
[PERSON] [71] develop a face detection system capable of accurately identifying individuals whether they are wearing masks or not. This enhancement addresses the increased use of face masks in public due to the COVID-19 pandemic. The system leverages a modified Caffe-MobileNetV2 (CMNV2) architecture, where additional layers are integrated for the better classification of masked and unmasked faces using fewer training parameters. The focus is on detecting facial features visible above the mask, such as the eyes, ears, nose, and forehead. The model demonstrated high accuracy, achieving 99.64% on static photo images and similarly robust performance on real-time video.