## 2 Literature Review  
For law enforcement, FR is an appealing area of research and development. Surveillance cameras are used in conjunction with intelligence techniques worldwide to detect criminal activity. Currently, as the epidemic of COVID-19 spreads all over the world, people are forced to cover most of their faces with masks to contain the pandemic, requiring much more accurate face recognition algorithms for identity verification. Factors associated with biometric sample capture and presentation, such as facial occlusions, have a significant impact on the precision of FR algorithms [10; 11]. Past studies showed different problems exist in recognizing human faces in real-time: (1) Face pose: Computerized systems are highly sensitive to pose variations. When a person's head and viewing angle vary, so does his or her facial position. (2) Illumination condition: The variation in lighting conditions has a significant impact on the quality of an image. (3) Face occlusion: The biggest challenge for computer vision systems is recognizing human faces when they are covered with masks. (4) Expressions: Varied conditions cause multiple human moods, which lead to the display of various emotions and, subsequently,changes in facial expressions. (5) Aging: The appearance of a person's face varies over time and reflects their age, which is a new problem for facial recognition algorithms. Researchers have presented techniques for occluded face recognition [12]. These authors developed an automatic facial recognition solution. The settings involved masked probes, unmasked pairs, masked pairs, and unmasked references with actual and synthetic masks. In [13], the author developed an end-to-end FR network that is insensitive to face masks and invariant to face images. First, face mask synthesized datasets were created by accurately matching the face mask to images in publicly available datasets, namely LFW, CASIA-Web Face, CFP, CPLFW, and CALFW. Afterward, datasets were used to generate training and testing datasets. Second, they introduced a model consisting of two components: an alignment component and a feature extraction component using DCNN to generate a 512-feature vector. The network is invariant to face images with a face mask since these modules are trained end-to-end. Their experimental work showed significant improvement compared with state-of-the-art systems. The authors of [14] proposed a CNN model for face detection based on facial features. They developed a new method for detecting faces based on the spatial structure and arrangement of facial components' responses. The grading system is data-driven, and it was carefully crafted to account for difficult circumstances where faces are only partially visible. Faces with extreme occlusion and unrestricted pose fluctuations are detected by their CNN architecture. On well-known benchmarks, namely, AFW, PASCAL Faces, WIDER FACE, and FDDB, their technique performs admirably. In [15], the authors proposed a set of repurposed datasets as well as a standard for researchers to employ. They also presented a pre-training method based on visual representation learning tailored to unmasked vs. masked face matching. Their research discovered robust traits that might be used to distinguish people in a variety of data collection circumstances. This was accomplished by training on a variety of datasets and confirming the results using a variety of holdout datasets. When it came to masked-to-unmasked face matching, their method's specific weights outperformed conventional face recognition features. The authors introduced a mask-aware FR system in [16] that can distinguish between people wearing and not wearing facial masks. They evaluated three traditional descriptors, such as local binary pattern (LBP), local directional order pattern (LDOP), and histogram of oriented gradients (HOG), along with support vector machine (SVM) for face mask recognition.