# Effective Attention-Based Mechanism for Masked Face Recognition
## 1 Introduction  
Face recognition (FR) has represented one of the most important research topics for many years. Many researchers [1; 2; 3; 4; 5; 6] have introduced robust methods to solve the FR problem. The trend of developing methods for FR appears to have almost reached its peak at the time of this writing. Influenced by the convolutional neural networks (CNNs), the current algorithms using deep learning methods [1; 2; 3; 4; 5; 6] have achieved superior accuracy for FR. Systems based on FR are widely used in many areas across the world including airports, community gates, and healthcare; FR is also employed in some authentication applications, such as face-to-face attendance monitoring and mobile payment systems based on face profiles. With the emergence of the COVID-19 pandemic, a viral infection caused by severe acute respiratory syndrome [7] has spread globally and brought many major challenges to daily human activities. To avoid COVID-19 infection, many people have worn and continue to wear masks. Mask wearing affects current FR application systems because the human face--the target of interest--is partially covered. In real-world FR applications, face occlusion, particularly masked face occlusion, will significantly affect existing FR performance and decrease re-identification accuracy [8]. Modern deep learning-based models are advanced enough to extract face features and learn the important key features such as face edges, mouth, nose, and eyes [9]. However,the presence of a facial mask occludes most of the key features, thus complicating the feature extraction process. Since traditional methods for FR have been designed specifically to work with all face information available, a mask on a face makes the models lose about 50% of the useful information [10]. The facial mask blocks important features such as the mouth and nose, thus obstructing the face feature structure, as reported in [11]. This specific issue has recently emerged as a particularly serious barrier to the field of FR. Therefore, to solve this problem, novel methods must be invented, or the existing algorithms must be modified substantially. Initially, facial mask recognition has been attempted, and researchers have introduced many robust solutions [12; 13; 14] to detect facial masks. Many scholars have recently presented various methods that address the MFR problems using deep learning techniques [9; 11; 15; 16; 17; 18; 19; 20]. [PERSON] et al. summarized the various MFR methods that have recently been proposed [21]. Further, because of the insufficient availability of masked face images for model training and testing, studies have proposed several masked face datasets [11; 22; 23] and data augmentation tools for generating simulated masked images [9; 11]. MFR represents a special case in the occlusion FR domain. In contrast to regular occluded FR, MFR involves three major challenges: the key features of the face, such as the mouth, nose, and chin, are occluded; most of the FR methods have been designed specifically to work with all face features available, and there is currently no publicly available large-scale masked face training and testing benchmark dataset. Moreover, most existing methods have been developed for the specific masked face datasets used in their development. Thus, a specific method may perform well for a limited dataset whereas it performs badly for other datasets. Further, the average accuracy of the existing MFR methods is only 89.5% [21]. With this background, it is necessary to develop a method that can consistently achieve good results on all datasets. Recently, the methods based on attention mechanism are widely used to solve various problems in vision tasks such as image classification [24], age-invariant face recognition [25; 26], and specifically face recognition with masked face [11; 19]. It should also be noted that the existing MFR methods which are based on attention demonstrate high accuracy compared to other methods.