# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 4 Distributions of the Training and Testing Data
In this setting, all the evaluation metrics scored significantly lower scores than in the other experiments under the remaining settings. This indicated that personal identification using ear images captured from a different direction from that of the training data was more challenging compared with the other experimental settings. Conversely, in all cases under experiment setting ii, we obtained higher scores that were statistically significant compared with the random-identifica tion cases (\(p<0.0005\) using the Mann-Whitney U test). Therefore, despite the reduced identification accuracy, the results indicated the feasibility of achieving personal identification under this setting. Experiment setting (iii) comprised an experiment in which the ear images captured from all directions were deployed as the training data, and those captured from each direction and all directions were deployed as the test data. Under this setting, scores exceeding 0.964 were achieved for all evaluation metrics across all experiments, indicating that, by training the model using ear images captured from all directions, individuals can be identified regardless of the direction from which the images were captured. The results above indicate that high identification accuracy was achieved in experimental settings (i) and (iii), whereas experimental setting (ii) showed considerably lower identification accuracy. Figure 5 presents the learning curves for experiments H_H (experimental setting (i)), H_L (experimental setting (ii)), and A_A (experimental setting (iii)). As shown in this figure, when the training and validation data were captured from different shooting directions, as in the case of H_L, the model's learning did not proceed effectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experimental Setup** & **Experiment Name** & **Number of Test Images** & **Number of Train Images** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{3}{*}{i} & H\_H & 1160 & 120 & 0.982 & 0.981 & 0.984 & 0.983 \\  & E\_E & 1160 & 120 & 0.978 & 0.975 & 0.978 & 0.977 \\  & L\_L & 1160 & 120 & 0.996 & 0.996 & 0. This is likely the reason for the low accuracy observed in the identification test. Conversely, when the training and validation data were captured from the same shooting direction, as in the cases of H_H and A_A, the model learned effectively and achieved high identification accuracy. Therefore, it can be inferred that the high accuracy observed in the identification test is a result of proper model training under these conditions. ### Results of the Visualization for the Identification Basis

Figure 6 shows the heatmap of the identification evidence obtained using Grad-CAM. As per the Grad-CAM results, the regions that contributed strongly to the identification process are shown in red, whereas those with weak contributions are shown in blue. Except for a few experiments (H_H, H_E, and H_L), most of the experiments focused on the contour of the ear for personal identification. Furthermore, when E (the ear position) and L (low position) were deployed as the training data, the features inside the auricle, such as ridges and depressions, contributed to the identification process. Figure 5: Learning curves for the three experiments: (**a**) H_H, (**b**) H_L, and (**c**) A_A. Figure 6: Grad-CAM results. Subfigures (**i**-**iii**) show the Experimental Setup described in Section 5. ## 9 Discussion

Experimental settings (i) and (iii) were conducted under conditions where the training and testing data included ear images captured from the same shooting direction. As presented in Table 4, these experiments obtained high identification accuracies across all evaluation metrics, indicating that the model could accurately distinguish ear images captured from the directions on which it had been trained, thereby confirming the hypothesis regarding the feasibility of personal identification based on ear images captured from multiple directions.