# Effective Attention-Based Mechanism for Masked Face Recognition
## 2 Related Works  
With the success of FR research, researchers have continued to focus on the challenges posed by occluded face recognition [17; 27; 28]. The recognition of an occluded face is challenging because the human face can be covered by visual obstacles of any size or shape appearing anywhere [29]. With the COVID-19 pandemic, MFR has become one of the greatest challenges in the FR domain. MFR is a specific facial occlusion problem since the essential parts of the face, such as the mouth, nose, or chin, are occluded. The objective of research on MFR is to identify or verify the specific identity of a person when people are wearing a facial mask. Some of the existing methods that researchers have proposed to solve occluded face recognition and MFR problems are described in this section. [PERSON] et al. [17] presented a technique to address partial occlusion by discovering and disposing of corrupted face feature elements for recognition. This study decomposed the face recognition challenge under random partial occlusions in three stages: First, they developed a pairwise differential Siamese Network (PDSN) to capture the differentiation in the face features between the occluded and non-occluded face pairs. Second, they built a masked dictionary for masked features that they obtained from the previous stage to composite the feature discarding mask (FDM). Third, a combination of the FDM of random partial occlusions from the dictionary is multiplied by the original feature to eliminate the effect of partial occlusions from recognition. This approach aims to remove the occluded areas from depth features. However, it is difficult in practice to meet the requirements of the matched image. Various studies have adopted restoration-based methods [30; 31; 32; 33] to restore the missing part of the face image and reconstruct a new face image from the training dataset. Since generative adversarial nets (GANs) were first introduced [34], many researchers have used GAN methods to address facial occlusion problems. [PERSON] et al. [35] proposed a method that involved generating the corrupted pixel(s) and reconstructing the missing content. [PERSON] et al. [18] proposed a model that can detect and remove the mask to provide complete, unobstructed facial images. First, the model detects the mask region and produces it as binary segmentation. Then, it uses two discriminators based on the GAN network to learn the global structure and missing part of the facial image. However, these approaches have not evaluated the recognition performance of their models. In contrast to the previous GAN-based methods, [PERSON] et al. [36] presented an algorithm framework that consists of de-occlusion and distillation modules. The de-occlusion module uses GAN to perform masked face completion, which recovers the occluded features beneath the mask and eliminates the appearance uncertainty. The distillation module uses a pre-trained model to perform face classification. On the simulated LFW dataset, their highest accuracy for recognition performance is 95.44%. MFR became an urgent research topic to consider during the COVID-19 epidemic. [PERSON] et al. [15] proposed a new framework with which to handle the MFR problem that used a deep network based on ResNet-50 [37]. The authors trained the network using the small Real-world Masked Face Recognition Dataset (RMFRD) described in [22]. However, this method did not yield adequate results because the network used only works with non-occlusion faces. [PERSON] and [PERSON] [9] presented a similar strategy using FaceNet [1], a deep network-based face recognition system, to train with their dataset VGGFace2-mini-SM1. They used their own proposed simulated masked face dataset to train the network. This method produced better results than the first method since they trained with a large dataset from scratch. Meanwhile, [PERSON] et al. [38] used ArcFace [5], a deep network-based face recognition system, to train with their simulated dataset.