## 2 State-of-the-Art Overview
A multitask learning architecture, in which a single network is trained to perform mask classification and face recognition tasks, is another technique successfully applied to mask recognition [54]. This strategy has also been found to increase the precision of mask face recognition by using mask information to aid in the recognition process [55]. For example, in [56], the authors suggested a multitask learning architecture and attained a recognition accuracy of 96.2%. Another approach is to employ generative models, such as Generative Adversarial Networks (GANs) [57] or Variational Autoencoders (VAEs) [58], to generate a varied set of masked faces for fine-tuning or training new models. A similar VAE-based method has also been shown to improve the accuracy of masked face recognition, although it depends on the availability of a large and diverse collection of unmasked faces [59]. For example, in [60], the authors used a GAN to generate masked faces and attained a recognition accuracy of 94.5%. A popular strategy is to remove the mask from \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Recognition Rate** \\ \hline Principal component analysis with ANN face recognition system [38] & 95.45\% \\ Deep Dense Face Detector [39] & 91.79\% \\ Radial Basis Neural Network [40] & 97.56\% \\ Convolutional Neural Network [41] & 85.1\% \\ Branch Convolutional Neural Network [42] & 97.2\% \\ Radial Basis Function Network [43] & 97.75\% \\ Region-based Convolutional Neural Networks [44] & 99\% \\ Rotations Invariant Neural Network [45] & 90.6\% \\ Gabor Wavelet [46] & 99.94\% \\ Weightless neural networks [47] & 89.22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of methods by recognition rates. the face before applying a facial recognition model to the unmasked face [61]. This is accomplished by training the deep learning model to create an unmasked face from the input masked face. For example, [PERSON] et al. [62] explored facial action recognition and face identification applications and discovered that both benefit from the encoding of face photos using Gabor wavelets. They performed dimensionality reduction and a linear discriminant analysis on the down-sampled data. Gabor wavelet faces can help to enhance discrimination. The closest feature space is expanded using several similarity measures. [PERSON] et al. [63] proposed a uniform framework to identify both masked and unmasked faces. They proposed rectification blocks to correct features extracted by a cutting-edge classification method in both the spatial and channel dimensions to reduce the distance in the corrected feature space between a masked face and its mask-free equivalent. Other approaches, such as Region-based CNN, Two-stream CNN, and 3D CNN, have been proven to increase the recognition accuracy of masked faces. Ref. [64] used a Region-based CNN to extract characteristics from the masked face, which were subsequently put into a fully connected layer for classification. On a masked face dataset, their approach achieved an accuracy of 96.2%. In [65], a 3D CNN was used to learn spatial-temporal information from the masked face, which was subsequently input into a fully connected layer for classification. On a masked face dataset, the approach attained an accuracy of 98.5%. Although these approaches have increased masked face recognition accuracy, they still depend on the availability of a large and diverse collection of masked faces. In [66], to recognize faces of persons in mines, avalanches, under water, or other hazardous settings where their face may not be highly visible over the surrounding background, a lightweight CNN architecture was presented. The created model supports mobile devices as easily as possible. A box is displayed on the device's screen as the processing output at the face location. The findings demonstrate that the proposed lightweight CNN recognized human faces over a range of textures with an accuracy of more than 95%.