## 4 Experimental Analysis
### Experiment #2  
The second experiment aimed to propose an alternative strategy for enhancing the performance of the fine-tuned and augmented ResNet-50 model. Specifically, we advocate incorporating the Mean-CAM framework with the ResNet-50 model. As expounded in Section 3, Mean-CAM enhances the representation of ear images by guiding the model's attention towards the most salient common area, signifying the most pertinent features. The investigation revolves around assessing whether Mean-CAM-CNN can yield performance improvements in recognition compared to utilizing a conventional CNN alone. The performance of the proposed Mean-CAM-CNN framework was evaluated by scrutinizing the representativeness of various threshold values of the \(\tau\) parameter. The evaluation protocols employed in these experiments are detailed in Section 4.2. Table 2 presents the Rank-1, Rank-5, and AUCMC results for the MAI and AWE datasets, with bold values highlighting the optimal results for each respective metric. Additionally, Figure 10 depicts the CMCs, illustrating the performance of ResNet-50 features and the proposed Mean-CAM-CNN framework, considering different threshold values of the \(\tau\) parameter, across the two utilized datasets. In this experiment, we observe that the performance results of ResNet-50 and Mean-CAM-CNN, with various values of the \(\tau\) parameter, are closely comparable for the MAI dataset, displaying marginal variations. Conversely, for the AWE dataset, Mean-CAM-CNN consistently outperforms ResNet-50. The highest Rank-1 identification accuracies achieved were 99.67% for the MAI dataset and 74.50% for the AWE dataset. Notably, the optimal configuration, yielding these top Rank-1 recognition rates, corresponds to Mean-CAM-CNN with a \(\tau\) parameter set to 0.5. This configuration demonstrates superior performance across both datasets and all utilized performance metrics. In comparison to the conventional ResNet-50 model, the best Rank-1 recognition rates attained using Mean-CAM-CNN increased by 1.01% for the MAI dataset and 16.75% for the challenging \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Architecture** & **MAI** & **AWE** \\ \hline \multirow{4}{*}{**Rank-1 (\%)**} & **AlexNet** & 88.50 & 30.25 \\ \cline{2-4}  & **VGG-16** & 95.50 & 47.25 \\ \cline{2-4}  & **VGG-19** & 91.50 & 40.25 \\ \cline{2-4}  & **ResNet-50** & **98.66** & **57.75** \\ \hline \multirow{4}{*}{**Rank-5 (\%)**} & **AlexNet** & 95.50 & 50.25 \\ \cline{2-4}  & **VGG-16** & 99.50 & 73.75 \\ \cline{2-4}  & **VGG-19** & 97.50 & 66.75 \\ \cline{2-4}  & **ResNet-50** & **99.66** & **79.00** \\ \hline \multirow{4}{*}{**AUCMC (\%)**} & **AlexNet** & 92.11 & 56.54 \\ \cline{2-4}  & **VGG-16** & 94.56 & 75.41 \\ \cline{2-4}  & **VGG-19** & 93.91 & 72.44 \\ \cline{2-4}  & **ResNet-50** & **100.00** & **96.54** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative evaluation of fine-tuned CNN architectures for ear recognition. AWE dataset. The noteworthy performance of Mean-CAM-CNN, particularly with the challenging AWE dataset, can be attributed to its capacity to focus the CNN's attention solely on pertinent information, thereby eliminating irrelevant details.