## 3 Materials and Methods
### Data Acquition  
The data are gathered from a variety of popular datasets available on the Internet, as described in Table 2. Faces with masks appear in a small number of datasets. As a result, an augmentation approach is used on multiple common verification datasets to create the synthesized face mask evaluation dataset. The data augmentation technique is applied to LFW [31], CALFW [32], CPLFW [33], and CFP [34]. The LFW (Labeled Faces in the Wild) is a popular public face verification benchmark containing 13K photos and 5.7K IDs. To analyze the performance of the suggested AFR-Conv, 8500 face photos with masks were employed in total. Cross-Age LFW (CALFW) is a revision of LFW that stresses the age disparity between positive couples even more to increase intra-class variation. CPLFW (cross-pose LFW) is a revision of LFW that stresses pose differences to increase intra-class variation. Frontal-Profile (CFP) is a FR dataset created to aid studies in the challenge of in-the-wild frontal-to-profile face verification. The CFP's frontal-profile and frontal-frontal verification pairings are employed in this paper. Only frontal face pictures are synthesized using face masks due to the high percentage of unsuccessful landmark detections in profile photographs. Figure 1(a) illustrates an example of the LFW dataset's generated face mask-enhanced pictures.  
To avoid overfitting, data augmentation is used. To increase the variance in the training dataset, the data are augmented by mirroring and cropping the photos. Each preprocessed face picture in the training set is supplemented into four images after the preprocessing stage by rotating the input image in four directions: 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). Augmentation aids in boosting data size, producing new data from existing data, and overcoming the absence of labeled pictures. The Real-World Masked Face Dataset (RMFD) [35] is used in addition to the synthetic versions of typical face recognition testing datasets. RMFD is the world's largest masked face dataset at the time of writing. From cleaned and annotated photos scraped from the internet, the dataset comprises 5000 masked faces of 525 individuals and 90,000 normal faces. Figure 1(b) depicts photos from the RMFD dataset with and without a face mask.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **\#Images** & **\#Identities** & **Select Images** & **Web Link** \\ \hline CASIA-Webface [30] & 494,414 & 10,575 & 600 & [[https://paperswithcode.com/dataset/csia-webface](https://paperswithcode.com/dataset/csia-webface)]([https://paperswithcode.com/dataset/csia-webface](https://paperswithcode.com/dataset/csia-webface)) (accessed on 23 February 2022) \\ LFW [31] & 13,233 & 5749 & 2000 & [[http://vis-www.cs.umass.edu/lfw/](http://vis-www.cs.umass.edu/lfw/)]([http://vis-www.cs.umass.edu/lfw/](http://vis-www.cs.umass.edu/lfw/)) (accessed on 23 February 2022) \\ CALFW [32] & 12,174 & 4025 & 1000 & [[http://whdeng.cn/CALFW/index.html](http://whdeng.cn/CALFW/index.html)]([http://whdeng.cn/CALFW/index.html](http://whdeng.cn/CALFW/index.html)) (accessed on 23 February 2022) \\ CPLFW [33] & 12,174 & 4025 & 3000 & [[http://www.whdeng.cn/cplfw/index.html](http://www.whdeng.cn/cplfw/index.html)]([http://www.whdeng.cn/cplfw/index.html](http://www.whdeng.cn/cplfw/index.html)) (accessed on 23 February 2022) \\ CFP [34] & 10 per identity & 500 & 400 & [[http://www.cfpw.io/cfp-dataset.zip](http://www.cfpw.io/cfp-dataset.zip)]([http://www.cfpw.io/cfp-dataset.zip](http://www.cfpw.io/cfp-dataset.zip)) (accessed on 23 February 2022) \\  & 4 profiles/identity & & & (accessed on 23 February 2022) \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 2: Different public face datasets statistics and selected images for experiments.