## 4 Results and Discussion  
### Results for Model 1 on Dataset 1  
Figure  
Figure 12: Gender Classification for Model 1. Figure 13: Model 1 Performance in Gender Classification. Figure 14: Confusion Matrix of Model 1 Performance in Gender Classification. Figure 15: Age and Ethnicity Classification. Figure 16: Confusion Matrix of Model 2 Performance in Ethnicity Classification. Figure 17: Model 2 Performance in Ethnicity Classification. Figure 19: Performance of Gender Classification Model 1 on Dataset 2. Figure 18: Gender Classification on Dataset 2. Figure 21: Ethnicity Class Model 2. Figure 20: Confusion matrix of Performance of Gender Classification Model 1 on Dataset 2. Figure 13:0 Ethnicity Classification for Dataset 2. Figure 13:1 Ethnicity Classification for Model 2. Table 3 shows the results of performing a 10-fold cross-validation for Model A and Model B with respect to their training accuracy and testing accuracy. For Model A, which is focused on gender classification, the training accuracy and testing accuracy are reported  
Figure 13:2 Model 2 Performance for Ethnicity Classification. Figure 13:3 Confusion Matrix of Model 2 Performance for Ethnicity Classification. for each fold. Each fold represents a different partitioning of the dataset, where the model is trained on a subset of the data (training accuracy) and evaluated on a separate subset (testing accuracy). The values range from 0 to 1, with higher values indicating better performance. The average values are also calculated to summarize the overall performance of the model across all folds. In this case, the average training accuracy is 0.82 and the average testing accuracy is 0.80. In Table 4 for Model B, which is focused on ethnicity classification, the same pattern applies. The training accuracy and testing accuracy are reported for each fold, representing the performance of the model on different partitions of the dataset. The average training accuracy for Model B is 0.73, and the average testing accuracy is 0.70. These results provide insights into how well the models perform in classifying gender and ethnicity based on the given dataset. The training accuracy indicates how well the model fits the training data, while the testing accuracy provides an estimate of how well the model generalizes to new, unseen data. The average values give an overall assessment of the model's performance across different partitions of the dataset. In Table 5, \"Model 1\" and \"Model 2\" refer to the two different models used for gender and ethnicity classification, respectively. \"Dataset 1\" and \"Dataset 2\" represent the two different datasets used in the experiments. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Fold** & **Training Accuracy** & **Testing Accuracy** \\ \hline
1 & 0.75 & 0.72 \\
2 & 0.74 & 0.71 \\
3 & 0.72 & 0.70 \\
4 & 0.73 & 0.70 \\
5 & 0.75 & 0.72 \\
6 & 0.73 & 0.70 \\
7 & 0.71 & 0.68 \\
8 & 0.74 & 0.71 \\
9 & 0.76 & 0.73 \\
10 & 0.72 & 0.69 \\ Average & 0.73 & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model B—Ethnicity Classification. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Fold** & **Training Accuracy** & **Testing Accuracy** \\ \hline
1 & 0.82 & 0.80 \\
2 & 0.84 & 0.81 \\
3 & 0.81 & 0.79 \\
4 & 0.83 & 0.82 \\
5 & 0.85 & 0.83 \\
6 & 0.82 & 0.80 \\
7 & 0.80 & 0.78 \\
8 & 0.83 & 0.82 \\
9 & 0.85 & 0.83 \\
10 & 0.81 & 0.79 \\ Average & 0.82 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model A—Gender Classification. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Dataset** & **Augmentation Strategies** & **Training Accuracy** & **Testing Accuracy** & **Training Loss** & **Testing Loss** \\ \hline Model 1 & Dataset 1 & Without Augmentation & 0.82 & 0.80 & 0.34 & 0.37 \\ Model 1 & Dataset 1 & With Augmentation & 0.86 & 0.84 & 0.20 & 0.23 \\ Model 2 & Dataset 1 & Without Augmentation & 0.72 & 0.70 & 0.80 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results and Performance Variation with Augmentation Strategies.