# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 4 Distributions of the Training and Testing Data
Fourth, the ear-image preprocessing performed in this study only comprised manual cropping, which is time-consuming and may cause reproducibility issues. To address this, methods such as [PERSON] et al. 's RetinaFace [46] could be employed for the automatic detection of ear landmarks and accurate identification of ear positions based on the training data, thereby automating the cropping process. ## 10 Conclusions

This study was conducted to test the following hypotheses:

1. Personal identification can be achieved with ear images captured from multiple directions. 2. Valuable feature points exist on the ear and facilitate personal identification regardless of the shooting direction. To achieve our objectives, we constructed a CNN model for ear images captured from multiple directions, conducted identification experiments, and employed Grad-CAM for the visualization of the features that contributed to personal identification. The results of the identification experiments demonstrated that the model could achieve high identification accuracies of \(\geq\)0.980 across all evaluation metrics, confirming the feasibility of personal identification based on ear images captured from multiple directions. Our visualization experiments revealed that the helix accounted for key feature points in 97.5% and 56.0% of the frontal- and occipital-view images, respectively. These results indicated that the helix could be a valuable feature point that is independent of the shooting direction. However, the lower percentage obtained for the occipital view indicated that further studies are required to identify the other feature points that are consistently significant across all angles. Our findings demonstrate the feasibility of personal identification using multidirectional-captured ear images as well as contribute to the development of future identification methods that leverage partial or low-quality ear images and specific biometric features. Future studies should address the issues of data imbalance and the roles of external factors, such as hair, lighting conditions, and accessories, which may affect identification accuracy. Additionally, the development of automated preprocessing techniques, including ear-contour detection and feature extraction, would enhance the practicality and scalability of ear-based biometric systems. Conceptualization, R.F. and Y.O. ; methodology, R.F. and Y.O. ; validation, R.F. and Y.O. ; formal analysis, R.F. ; investigation, R.F. ; resources, R.F. ; data curation, R.F. ; writing--original draft preparation, R.F. and Y.O. ; writing--review and editing, R.F, Y.O, C.P. and Y.Y. ; visualization, R.F., C.P. and Y.Y. ; supervision, Y.O. ; funding acquisition, Y.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Muroran Institute of Technology Ethical Review Board (protocol code R6-07-507 and date of approval: 1 August 2024). Informed Consent Statement: Informed consent was obtained from all the subjects involved in the study. The participants were provided with detailed information about the purpose and procedures of the study, after which they provided written informed consent before data collection. The data presented in this study are not publicly available due to privacy, and the ear images collected for this study cannot be made public. However, the CNN model, program files, and a README file detailing their usage are available on GitHub (software version: ver1.0) at [[https://github.com/RyuhiFukuda/Ear_Classification](https://github.com/RyuhiFukuda/Ear_Classification)]([https://github.com/RyuhiFukuda/Ear_Classification](https://github.com/RyuhiFukuda/Ear_Classification)) (accessed on 13 February 2025). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: