# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 4 Distributions of the Training and Testing Data
## 5 Experimental Settings

High identification accuracies were maintained even for ear images captured from angles different from those employed during training for a practical perspective. This study was conducted to investigate the feasibility of personal identification using multidirectionally captured ear images. Therefore, in this experiment, we evaluated the identification accuracies when the ear images employed for training and testing were captured from the same and different directions. Table 3 summarizes the experimental settings. As shown in this table, the experiment proceeded under the three following configurations:

1. An experiment in which the ear images captured from the same direction as that of the training data were used as the test data. 2. An experiment in which the ear images captured from a different direction from that of the training data were used as the test data. 3. An experiment in which the ear images captured from all directions were used as the training data, and those captured from each direction were used as the test data. As presented in Table 3, the ear images captured from above, the same height as, and below the ear were denoted as H, E, and L, respectively. Additionally, each identification experiment was denoted as training_testing. For instance, Experiment E_H refers to that in which the training and test data were E and H, respectively. Overall, 13 experiments were conducted under three experimental conditions. By conducting identification experiments with different combinations of training and test data, the generalization ability of the model \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Setup** & **Training** & **Test** & **Experiment Name** \\ \hline \multirow{3}{*}{i} & H & H & H\_H \\  & E & E & E\_E \\  & L & L & L\_L \\ \hline \multirow{3}{*}{ii} & E & H & E\_H \\  & E & L & E\_L \\  & H & E & H\_E \\  & H & L & H\_L \\  & L & H & L\_H \\  & L & E & L\_E \\ \hline \multirow{3}{*}{iii} & A & H & A\_H \\  & A & E & A\_E \\ \cline{1-1}  & A & L & A\_L \\ \cline{1-1}  & A & A & A\_A \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Train and test data employed for the experiment. can be evaluated, and identification performance, independent of angles, can be confirmed. This reveals a model that can adapt to a broader range of data without relying on ear images taken from a specific angle. Furthermore, by clarifying the feature points that contribute to identification at each angle, it becomes possible to identify which features most significantly affect identification accuracy. This provides insights for future model improvements. As a result, it allows for a focused and experimental evaluation of the key features that should be emphasized to further enhance identification accuracy. ## 6 Identification Experiments

We performed the model construction, validation, and identification tests on a workstation equipped with an Intel Core i7-10700F (2.90 GHz) processor (Intel Corporation, Santa Clara, CA, USA) and an NVIDIA GeForce RTX 3060 (8 GB) graphics card (Santa Clara, CA, USA). In each experiment, corresponding to Table 3, we evaluated the identification performance using ear images captured from multiple directions by performing five-fold cross-validation. In this study, we used four metrics for the evaluation, i.e., accuracy, recall, precision, and the F1-score:

\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{2}\]

\[\text{Recall}=\frac{TP}{TP+FN} \tag{3}\]

\[\text{Precision}=\frac{TP}{TP+FP} \tag{4}\]

\[\text{F1}-\text{score}=\frac{2\times Recall\times Precision}{Recall+Precision}. \tag{5}\]

Here, TP denotes the number of target images classified into the correct class, TN denotes the number of non-target images classified into the correct class, FP denotes the number of target images classified into the wrong class, and FN denotes the number of non-target images classified into the wrong class. ## 7 Visualization for the Identification Basis

In this experiment, we investigated the rationale for the identification of ear images.