## 4 Experimental Analysis
### Experiment #2
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**AMI**} & \multicolumn{3}{c}{**AWE**} \\ \cline{3-8}  & & **Rank-1 (\%)** & **Rank-5 (\%)** & **AUCMC (\%)** & **Rank-1 (\%)** & **Rank-5 (\%)** & **AUCMC (\%)** \\ \hline \multirow{10}{*}{**Average**} & **ResNet-50** & 98.66 & 99.66 & **100.00** & 57.75 & 79.00 & 96.54 \\ \cline{2-8}  & \(\tau\) = **0.1** & 96.67 & **100.00** & **100.00** & 62.25 & 79.50 & 96.70 \\ \cline{2-8}  & \(\tau\) = **0.2** & 98.00 & **100.00** & 99.99 & 58.25 & 78.75 & 97.02 \\ \cline{2-8}  & \(\tau\) = **0.3** & 98.66 & 99.66 & 99.99 & 68.50 & 85.75 & 97.99 \\ \cline{2-8}  & \(\tau\) = **0.4** & 99.33 & 99.33 & 99.99 & 68.75 & 83.25 & 98.56 \\ \cline{2-8}  & \(\tau\) = **0.5** & **99.67** & **100.00** & **100.00** & **74.50** & **89.50** & **98.93** \\ \cline{2-8}  & \(\tau\) = **0.6** & **99.67** & **100.00** & **100.00** & 69.25 & 87.00 & 98.56 \\ \cline{2-8}  & \(\tau\) = **0.7** & 98.33 & **100.00** & **100.00** & 67.50 & 87.25 & 98.34 \\ \cline{2-8}  & \(\tau\) = **0.8** & 98.66 & 99.66 & 99.99 & 62.00 & 85.25 & 97.88 \\ \cline{2-8}  & \(\tau\) = **0.9** & 96.00 & 99.00 & 99.99 & 60.00 & 78.75 & 96.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ear recognition results using ResNet-50 and Mean-CAM-CNN across different threshold values of the \(\tau\) parameter. Figure 10: CMCs for the testing sets of (**a**) MAI and (**b**) AWE datasets: comparative analysis of ResNet-50 and Mean-CAM-CNN with varied threshold values (\(\tau\)). To highlight the effectiveness of the Mean-CAM technique in guiding the CNN to prioritize pertinent information, we provide visual demonstrations in Table 3. The results illustrate the significant impact of Mean-CAM in addressing misclassifications induced by the baseline model. Additionally, Mean-CAM enhances the prediction probability of the correct class. For instance, in the first AWE image presented in Table 3, Mean-CAM rectified the target class number from 41 to 95 by excluding irrelevant regions, while concurrently elevating the predicted probability of the correct class to 99.14%. Furthermore, in the first MAI, although the baseline model correctly identified the class, Mean-CAM augmented the prediction probability from 56.69% to 90.10%. This improvement underscores the efficacy of our proposed Mean-CAM framework. Our proposed approach is based on the use of a global stage to extract salient features. Specifically, the Mean-CAM method is used to delineate a region of interest in the image based on mean heatmaps derived from images of the same class. This means that each image belonging to a class has a significant contribution in constructing the relevant RoI based on the average of all extracted salient maps (see Figures 8 and 9). The resulting aggregation of all trained images belonging to the same class can direct the attention \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Original Image** & **Results** & **B/L Visualizations** & **Mean-CAM Visualizations** \\ \hline \hline \multirow{4}{*}{**MAI**} & \multirow{4}{*}{**Mean-CAM**} & \multirow{4}{*}{**Mean-CAM**} & \multirow{4}{*}{**Mean-CAM**} \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Visualization and performance analysis of model predictions: a comparative study using the Mean-CAM technique on ear images (B/L pred denotes baseline prediction, and **P** represents probability). of our model to the most frequent relevant region and away from the irrelevant ones. Consequently, this technique reduces potential misclassification by minimizing the intra-class variability, which makes the feature extraction task less sensitive to the variability of data in a class.