# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 4 Distributions of the Training and Testing Data
However, we utilized the ear images of only 10 subjects under identical conditions, even though maintaining completely uniform imaging conditions in real-world scenarios is challenging. Thus, measures must be put in place to address data imbalance. This issue could be mitigated by employing data-augmentation techniques to improve the balance among classes and generate a more uniform and sufficient dataset. Additionally, the utilization of the focal loss function [41; 42], which strengthens penalties for misclassification in minority classes, might help alleviate the effects of data imbalance. Furthermore, by training on larger and more diverse datasets, it is expected that a high-performance model that captures the general \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Author** & **Year** & **Method** & **Data Set** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **CMC** \\ \hline \multirow{3}{*}{[PERSON] [26]} & \multirow{3}{*}{2018} & Adjacent evaluation of & \multirow{3}{*}{IITD-II} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{0.986} \\  & & LBP & & & & & & \\  & & Adaptive LBP & WPUT & & & & & \\ \hline \multirow{3}{*}{[PERSON] [29]} & \multirow{3}{*}{2024} & CNN and bidirectional & IITD-II & - & 0.988 & 0.993 & 0.989 & - \\  & & LSTM & AMI & - & 0.941 & 0.943 & 0.928 & - \\ \hline \multirow{3}{*}{[PERSON] [28]} & \multirow{3}{*}{2024} & CNN & EarVNI.0 & 0.920 & 0.920 & 0.920 & 0.910 & - \\ \hline \multirow{3}{*}{This study} & \multirow{3}{*}{2025} & CNN & Original & 0.981 & 0.980 & 0.981 & 0.981 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of our results with those of the extant studies. characteristics of the ear can be constructed. By leveraging such a model through transfer learning, it could, for instance, enable personal identification across different ethnic groups. However, it is a known fact that differences in CNN architecture can significantly affect model performance. In particular, selecting the right CNN architecture is crucial for accurately capturing the important features of the ear from multiple angles. Moving forward, experimenting with various architectures will be necessary to further explore the optimal design for this task of personal identification using ear images captured from multiple angles. 2. Second, we did not consider the roles of external factors. Although the helix region was identified as a critical feature point, it could be affected by external factors such as hair or earrings. Furthermore, it is challenging to completely eliminate such external factors in practical applications. This issue could be resolved by integrating CNN with an attention mechanism [43] to reduce the influence of external factors and focus solely on learning the features of the auric. Nevertheless, if the helix or other critical feature points are largely obscured by hair or similar elements, our approach may not be applicable. In such cases, it would be necessary to switch to identification methods designed for other modalities, such as facial recognition. Additionally, ear-based personal identification is sensitive to lighting conditions, and accurate identification can be challenging, particularly in backlit or low-light environments. However, this challenge could be addressed by utilizing low-light image enhancement techniques [44], offering a solution to improve identification accuracy under such conditions. 3. Third, our method is based on deep learning, and the model size is extremely large. Therefore, addressing the computational load in real-time processing becomes a potential challenge. In large-scale surveillance systems, achieving practical identification performance requires not only solving the external factor constraints mentioned above but also addressing the computational load issue. To tackle this challenge, model compression techniques [45] that reduce the model size while maintaining identification performance could be effective. 4.