## 4 Experiments and Results
### Results  
The quantitative results of our proposed masked face real age estimation system are reported in this section. Our reported age estimation result is in the form of MAE. We evaluated our system on the synthetically generated masked IMDB-WIKI dataset to estimate the real/biological age of a person. In Table 2, we present the mean absolute error values achieved after a massive experiment was conducted, utilizing prominent off-the-shelf deep convolutional neural network models. From the comparison table, it is clearly evident that in all cases of experiments, hierarchical approaches perform marginally better than the singular approach. The comparative results of deployed CNN architectures are graphically presented in Figure 7.  
We investigated the performance of the deployed pre-trained CNN models for each particular age group (child, adult, and elder), which is quantitively shown in Table 3 and graphically presented in Figure 8. A pictorial presentation that mentions the quantitative performance (MAE) found over the synthetic masked face dataset, IMDB-WIKI, through both approaches is reported in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Singular Approach** & **Hierarchical Approach** \\ \hline ResNet 50 & 5.13 & 4.35 \\ WideResNet & 4.81 & 4.20 \\ DenseNet & 5.38 & 4.52 \\ MobileNet & 5.85 & 5.25 \\ ShuffleNet & 6.17 & 5.70 \\ SqueezeNet & 6.88 & 6.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean absolute error values regarding real age estimation over synthetic masked IMDB-WIK dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Child Group** & **Adult Group** & **Elder Group** \\ \hline ResNet 50 & 0.80 & 4.70 & 1.52 \\ WideResNet & 0.81 & 4.41 & 1.64 \\ DenseNet & 0.92 & 4.63 & 2.04 \\ MobileNet & 1.03 & 5.13 & 2.49 \\ ShuffleNet & 1.10 & 5.98 & 2.05 \\ SqueezeNet & 1.35 & 6.41 & 3.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Age-group-wise mean absolute error comparison of the experimented convolutional neural networks regarding real age estimation over synthetic masked IMDB-WIK dataset. Figure 7: The performance comparison of different CNN models in real age estimation. In Table 5, we compare our system performance with the existing methods concerning the mean absolute error achieved irrespective of whether faces are masked or non-masked. We only compare this with systems that estimate the biological age of a person rather than the apparent age or age group. We achieved better MAE in contrast to the works AGES [19], Kim et al. [63], OHRank [74], and CA-SVR [75], although our system handles face images with masks. As stated in the calculation procedure of the cumulative score (CS), the CS values on the synthetic masked face image dataset under distinct error thresholds are plotted in Figure 9. From the figure, a steady growth in CS value is observed if the allowable error thresholds increase. A comparison of cumulative scores between singular and hierarchical approaches is graphically presented in Figure 10. In this comparison curve, the score achieved using the WideResNet model is plotted, where the hierarchical approach exhibits \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Real Age** & **25** & **14** & **72** & **93** \\ \hline \hline Singular/Hierarchical & 21.61/25.01 & 17.24/14.94 & 63.57/73.06 & 90.52/92.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample images with corresponding mean absolute error evaluated by the best model. Figure 8: The performance of deployed models over age groups in terms of mean absolute error. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Dataset** & **Number of Images** & **Number of Subjects** & **Wearing Mask** & **MAE** \\ \hline AGES [19] & & & No & 8.83 \\ [PERSON] et al. [63] & & & No & 4.29 \\ OHRank [74] & & & No & 6.07 \\ CA-SVR [75] & & & No & 5.88 \\ Proposed & Imdb-Wiki & 523,051 & 20,284 & Yes & 4.20 \\ \hline \hline \end{tabular}
\end{table}