## 3 Materials and Methods
### System Implementation
We discuss the best results in terms of accuracy and loss and then compare our findings to those of other researchers in this field who have used various datasets to assess model performance. The experimental results of the ENSEMBLE-FRO approach are presented in terms of precision (PR), recall (RE), and detection accuracy (DA) metrics. The proposed APR-Conv-Ada is composed of three pre-trained DL architectures: Inception-v3, ResNeXt-50, and DenseNet-161. It achieves 94% for PR, 91% for RE, and 90% for DA on the 8500 selected face images. Table 4 shows that the proposed APR-Conv-Ada system better identifies human faces than other transfer learning algorithms such as ResNet-50, DenseNet-161, Ensemble-CNNs, and Inception-V3 because it makes fewer mistakes. Furthermore, APR-Conv-Ada using the AdaBoost model achieves 97.5% classification accuracy, recall, and precision. Figure 7: Accuracy versus loss with respect to train and test splits for proposed AFR-Conv-Ada model: (**a**,**b**) without fine-tune network and accuracy versus loss; and (**c**,**d**) with fine-tune network. The presented strategy, as shown in Table 5, outperforms the existing DL models. In this work, the proposed technique is compared with other current models in terms of accuracy and computing complexity. The comparisons were performed with VGG-16 [30] and Alex-Net [31] systems on our selected dataset. We have selected these two systems because they are easy to implement. Compared with these two systems, our method required a total processing time of 163 s. Overall processing times for the VGG-16 and Alex-Net were 184 s and 209 s, respectively. Based on these findings, it was determined that the proposed model took less time to identify faces. This shows that the proposed model is more efficient than its successors. The GPU is also used by Google Colab to test the computational performance of the proposed AFR-Conv-Ada system on this dataset. The GPU is used for high-performance computing. It can be thought of as a set of cores with a software layer that enables parallel processing. In contrast to the CPU, the GPU shows that its performance in terms of execution time is fast. The performance of several transfer learning algorithms is compared with the proposed AFR-Conv-Ada classifier in Table 6.

Table 7 shows the experimental results of different transfer learning algorithms compared with the proposed APR-Conv-Ada when face occlusion is 35% on testing and training datasets. Table 7 presents the experimental results when face occlusion is 35% on the testing and training datasets. As can be observed in Table 7, the three pre-trained DL architectures, Inception-v3, ResNeXt-50, and DenseNet-161, are compared in terms of precision (PR), recall (RE), detection accuracy (DA), and metrics when face occlusion is 35%. It can be observed that the F1-score for the ResNeXt-50 model achieves 89.5%. While the Inception-V3 model achieves 83.5%. The DenseNet-161 model achieves 90.5%, and it should be noticed that the F1 score for the APR-Conv-Ada model is 89.5%. Finally, the APR-Conv-Ada model achieves 98.5%. As shown in the results, due to the influence of the Ensemble-CNNs-W model and other models, the accuracy is highest on the F1 score when face occlusion is 35%. As it can be observed from Table 7, the F1-scores for the Ensemble-CNNs, Inception-V3, \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 & 120.5 s & 1.2 s & 0.4 s & 122.4 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average processing time on selected dataset with state-of-the-art systems using GPU.