# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Masked Face Recognition Approaches
#### 5.3.2 Masked Region Discarding  
Masked region discarding entails the removal of the masked area from the face image. This is achieved either by detecting the mask region and cropping out the masked portion or by using a predefined ratio to crop out a portion of the face that is typically unmasked. The remaining unmasked portion, usually containing facial features such as the eyes and forehead, is then utilized for training the recognition model. Table 7 provides a concise overview of the methodologies associated with this approach. In one of these methods, [PERSON] et al. [94] explored the Occlusion Removal strategy by investigating various cropping ratios for the unmasked portion of the face. They incorporated an attention mechanism to determine the optimal cropping ratio, aiming to maximize accuracy. In their study, the optimal ratio was identified as 0.9L, where L represents the Euclidean distance between the eye key points. The authors conducted experiments across four scenarios. In the first scenario, the model was trained using fully masked images and tested on masked images, achieving an accuracy of 91.529% with L = 0.9. The second and third scenarios involved training or testing with only one image masked, yielding accuracies of 86.853% and 82.533%, respectively, for L = 0.7. Finally, the fourth scenario adhered to the traditional face recognition approach, utilizing unmasked images for both training and testing purposes. [PERSON] [95] introduced a method that initially corrects the orientation of facial images and employs a cropping filter to isolate the unmasked areas. Feature extraction is conducted using pre-trained deep learning architectures including VGG-16, AlexNet, and ResNet-50. Leveraging pre-trained VGG-16 [97], AlexNet [98], and ResNet-50 [99] models for feature extraction in their masked face recognition approach, the effectiveness of these models in diverse image classification tasks has been well documented, showcasing their ability to attain high recognition accuracy. Feature maps are extracted from the final convolutional layer of these models, followed by the application of the bag-of-features (BoF) [100] methodology to quantize the feature vectors and create a condensed representation. The similarity between feature vectors and codewords is gauged using the Radial Basis Function (RBF) kernel. This approach demonstrates a superior recognition performance compared to other state-of-the-art methods, as evidenced by experimental evaluations on the RMFRD [17] and SMFRD [23] datasets, achieving accuracies of 91.3% and 88.9%, respectively. Furthermore, [PERSON] et al. [21] presented a comprehensive strategy aimed at overcoming the challenges associated with MFR through the introduction of innovative methodologies and datasets. Initially, the authors introduced the MFSR dataset, which includes masked face images annotated with segmentation and a diverse collection of full-face images captured under various conditions. To enrich the training dataset, they proposed the Identity Aware Mask GAN (IAMGAN), designed to synthesize masked face images from full-face counterparts, thereby enhancing the robustness of the dataset. Additionally, they introduce the Domain Constrained Ranking (DCR) loss to address intra-class variation, utilizing center-based cross-domain ranking to effectively align features between masked and full faces. Experimental findings on the MFSR dataset underscored the efficacy of the proposed approaches, underscoring their significance and contribution to the advancement of masked face recognition technologies. [PERSON] et al. [96] introduced