## 2 State-of-the-Art Overview  
This section first introduces some of the popular methods proposed for regular face recognition. Then, the state-of-the-art overview continues with masked face recognition. In general, the techniques to be applied in face recognition are quite simple [24]. Face characteristics can be extracted using principal component analysis or linear discriminant analysis, and then for example basic Euclidean distance with a backpropagated neural network can be employed to categorize face subjects [25]. Often, such facial recognition algorithms are subject to face-presenting assaults (face-PA), including print, video playback, and rubber masks [26]. To address the aforementioned issues, [PERSON] et al. [27] built a unique deep neural network to deep-encode face areas. Others used PCA to minimize the dimensionality of feature representation while eliminating redundant and contaminated visual information [28]. [PERSON] et al. [17] investigated the accuracy of face recognition and proposed the use of evolutionary algorithms to maximize the selection and prioritization of test cases, while machine learning guided the search for successful test cases. [PERSON] et al. [29] built a face detection and recognition system based on neural computing paradigms and artificial neural methods. The research findings indicated that the approach had a greater detection accuracy and a faster computation. [PERSON]'s team [30] suggested a technique based on multiscale facial components and the characteristics of the Eigen/Fisher artificial neural network, aiming to reduce the components of the face of various resolutions, such as eyes, nose, mouth, and the complete face, according to their saliency, and then apply the principal component analysis of the subspace or linear analysis to generate a vector of facial characteristics. [PERSON] et al. [31] suggested using preprocessing, cascade feature extraction, optimal feature extraction, and recognition as the four basic phases in convolutional processing. [PERSON] et al. [32] suggested an unsupervised neural network for the analysis of human activity as well as capturing faces. [PERSON] et al. [33] established the idea of candidate areas for faces. [PERSON] et al. [34] used the Ada boost face recognition system to scrutinize and retain all candidate regions. In [35], the candidate area was then classified using a small-scale CNN to determine whether it is a face and a medium-scale CNN to complete the categorization of all candidate regions. [PERSON] et al. [36] introduced a new deep dynamic neural network to assess and extract three key aspects of facial expression movies. The suggested model of [37] had recurrent network benefits and can be used to assess the sequence and dynamics of information in moving faces. Table 1 presents the recognition rate of different methods, tested on a regular face (without mask) dataset. Face recognition algorithms have evolved rapidly over the years due to a variety of causes [48]. Researchers have researched and created a variety of algorithms for occluded face identification in response to the unexpected aspects encountered in real world circumstances [49]. [PERSON] suggested a consistent subdecision network to obtain subdecisions that correspond to different facial areas and constraining subdecisions using weighted bidirectional KL divergence to focus the network on the upper faces without occlusion [50]. Fine-tuning current face recognition models on a dataset of masked faces is one of the most prevalent ways for masked face identification [51]. This strategy has been shown to improve the accuracy of masked face recognition, but it depends on the availability of a large and diverse collection of masked faces [52]. In [53], for example, scientists fine-tuned a face recognition model using a dataset of masked faces and reached a recognition accuracy of 92.5%.