## 3 Materials and Methods
### System Implementation
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** \\ \hline Resnet-50 & 89.5\% & 85.6\% & 90.5\% & 89.5\% \\ Inception-V3 & 86.2\% & 84.3\% & 85.5\% & 83.5\% \\ DenseNet-161 & 87.5\% & 86.5\% & 91.3\% & 90.5\% \\ Ensemble-CNNs & 89.5\% & 85.6\% & 90.5\% & 89.5\% \\ APR-Conv-Ada & 95.5\% & 97.6\% & 97.5\% & 98.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of different transfer learning algorithms compared with proposed APR-Conv-Ada method when face occlusion is 25% on testing and training datasets. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Deep Learning** & \multirow{2}{*}{**Training**} & **Attribute** & \multirow{2}{*}{**Prediction**} & \multirow{2}{*}{**Total Time**} \\
**Framework** & & & & \\ \hline VGG-16 [30] & 180.2 s & 2.0 s & 1.8 s & 184 s \\ Alex-Net [31] & 205.1 s & 2.2 s & 1.9 s & 209.2 s \\ AFR-Conv-Ada & 160.5 s & 1.8 s & 1.4 s & 163 s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average processing time on selected datasets with state-of-the-art systems using CPU. ResNeXt-50, and DenseNet-161 models are 87.5%, 81.5%, 89.2%, and 87.1%, respectively, while the AFR-Conv-Ada model achieves high performance (98.0% classification accuracy) when face occlusion is 35% on testing and training datasets. The proposed method obtained nearly the same accuracy as the above-mentioned classification systems; however, we tested our model on a considerably larger dataset that mostly met all real-world requirements. Table 8 shows even higher performance in verification by using AFR-Conv-Ada compared with other techniques. In this table, the proposed method is trained on a dataset with 45% face occlusion on the testing and training datasets. The proposed technique's verification performance is only marginally improved by training on the synthetic dataset. In this experiment, the synthesized CALFW training dataset was utilized to test the performance of the proposed system. In fact, after training with the synthesized dataset, recognition performance on the cross-age CALFW database declined. Rather, in all synthesized datasets in the table, the approach achieves significantly improved verification performance. The results of different transfer learning algorithms compared with the proposed AFR-Conv-Ada system for face occlusion are displayed in Table 8. This table indicates that the AFR-Conv-Ada approach improves verification performance in a similar way. In addition, the ROC curve is also used to measure the performance of the proposed classifier AFR-Conv-Ada in training and test datasets by 10-fold cross-validation. Figure 8 shows the ROC curve of the proposed method. The image alignment with an affine transform utilizing face landmarks developed by MTCNN is used to evaluate AFR-Conv-Ada [22]. The same hyperparameters as specified in [1] for training on the dataset are utilized for the AFR-Conv-Ada model trained on the face-mask synthesized CASIA-Webface. The 6000-face mask is utilized to generate image pairings derived from the original pairs for assessment in the LFW dataset, together with 10-fold cross-validation and the conventional unrestricted with labeled outside data technique. The half-synthesized pairs are constructed in this experiment to examine verification performance between face-masked images and normal images, with just the second image in each pair synthesized. Half-synthesized image pairs using original pairs and synthesized pairs presented by the database were also used in CFP-FF, CFP-FP, CALFW, and CPLFW evaluations. On the real-world dataset RMFD, the experiment is carried out by creating 800 mask-to-mask and mask-to-non-mask combinations at random, with equivalent negative and positive pairs. In images without face masks, non-mask-to-non-mask pairs are also generated and utilized for reference. As demonstrated in Figure 8, our training and validation accuracy continue to improve without reaching a point where the curve becomes