# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Face Mask Recognition Approaches
#### 5.1.5 YOLO (You Only Look Once)  
YOLO is a real-time object detection system that recognizes objects with a single forward pass through the neural network. This one-stage detector efficiently combines the tasks of object localization and identification, making it ideal for applications requiring rapid and accurate object detection, such as face mask recognition. YOLO balances speed and precision, adapting to various scenarios where quick detection is crucial. [PERSON] et al. [27] focus on face mask recognition using two advanced deep learning models, YOLOv3 and Faster R-CNN, to monitor mask usage in public places during the COVID-19 pandemic. They develop a dataset of about 7500 images categorized into masked and unmasked faces, which they manually label and enhance with bounding box annotations. This dataset includes various sources and is accessible online. Both models are implemented using Keras on TensorFlow and trained with transfer learning. The models detect faces in each frame and classify them as masked or unmasked, drawing colored bounding boxes (red or green) around the faces accordingly. Also, [PERSON] et al. [30] introduce SE-YOLOv3, an enhanced version of the YOLOv3 object detection algorithm, optimized for real-time mask detection by integrating Squeeze and Excitation (SE) blocks into its architecture. This modification helps focus the network on important features by recalibrating channel-wise feature responses, significantly improving detection accuracy. SE-YOLOv3 also employs GIoULoss for precise bounding box regression and Focal Loss to handle class imbalance effectively. Additionally, the model uses advanced data augmentation techniques, including mixup, to enhance its generalization capabilities. [PERSON] and [PERSON] [72] enhance the YOLO-v4 model for efficient and robust face mask recognition in complex environments, introducing an optimized CSPDarkNet53 backbone to minimize computational costs while enhancing model learning capabilities. Additionally, the adaptive image scaling and refined PANet structure augment semantic information processing. The proposed model is validated with a custom face mask dataset, achieving a mask recognition mAP of 98.3%. [PERSON] et al. [73] investigate an effective face mask recognition method using the YOLOv5 deep learning model during the COVID-19 pandemic. By leveraging a dataset of 853 images categorized into \"With_Mask\", \"Without_Mask\", and \"Incorrect_Mask\", the model is trained across different epochs (20, 50, 100, 300, and 500) to identify optimal performance. The results indicate that training the model for 300 epochs yields the highest accuracy at 96.5%. This approach utilizes YOLOv5's capabilities for real-time processing. Also, [PERSON] et al. [75] develop two versions: YOLOv5s-CA, with the CA module before the SPPF layer, and YOLOv5s-C3 CA, where CA replaces the C3 layers. Tested on a new dataset created from YouTube videos, YOLOv5s-CA achieves a [EMAIL_ADDRESS] of 96.8%, outperforming baseline models and showing promising results for real-time applications in monitoring mask usage during the COVID-19 pandemic. The study also includes an auto-labeling system to streamline the creation of training datasets. [PERSON] et al. [74] propose the FMDYolo framework which effectively detects whether individuals in public areas are wearing masks correctly, essential for preventing COVID-19 spread. It features the Im-Res2 Net-101 as a backbone for deep feature extraction, combined with the En-PAN for robust feature fusion, improving the model generalization and accuracy. The localization loss and Matrix NMS in the training and inference stages enhance the detection efficiency. [PERSON] [PERSON] et al. [76] propose an enhanced object detection model named AI-Yolo, specifically designed for accurate face mask recognition in complex real-world scenarios.