# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 4 Distributions of the Training and Testing Data
Conversely, experimental setting (ii), which proceeded under conditions where the training and test data included image data obtained from different directions, obtained significantly reduced identification accuracy compared with the remaining experimental settings. Nevertheless, the results were significantly higher than those obtained through random identification across all the evaluation metrics, indicating that the ear contains features that are crucial to personal identification even when the shooting direction differs from that of the training data. Figure 7 shows the anatomical parts of the ear. Based on the visualization results of Experiment A_A, which achieved high identification accuracies with ear images captured from all shooting directions, we analyzed the valuable feature points for identification. Tables 5 and 6 present the percentages of the red-highlighted regions on the Grad-CAM heatmaps of the frontal and occipital views of the ear images. These results indicated that the helix region yielded the highest percentage of discriminating evidence for the frontal and occipital views, indicating that the helix might be a valuable biometric feature regardless of the shooting direction. However, the helix only contributed to 56.0% of the identification process for occipital views. These results could not fully support the hypothesis regarding the existence of ear feature points that are universally key to identification regardless of the shooting direction. Table 7 presents a comparison of the identification accuracies obtained in this study and those obtained in the extant studies on ear-based personal identification. Notably, the datasets deployed for these studies differed in terms of the shooting direction constraints. For instance, the IITD-II dataset comprised frontal ear images, whereas the WPUT, AMI, and EarVNI.0 datasets included ear images captured from slightly off-frontal positions. Conversely, our dataset comprised ear images captured from 180\({}^{\circ}\) horizontal rotations and three different vertical heights (H, E, and L positions), making the identification task more challenging. The row labeled \"This study\" in Table 7 presents the results of the A_A experiment, which utilized this dataset. As presented in Table 7, our CNN model achieved comparable or superior identification accuracy despite the increased complexity of the deployed dataset. Therefore, we expanded the range of feasible shooting directions by demonstrating the feasibility of achieving accurate identification under a variety of ear-image-capturing conditions. Our findings highlight the potential of multidirectionally captured ear images for personal identification, offering new insights by visualizing the identification accuracies and underlying proof. The high-accuracy identification model based on multidirectionally captured ear images could be deployed as a complementary technology to conventional facial recognition systems. In scenarios in which the face is partially obscured by masks or affected by environmental lighting, the stable features of the ear could suffice to ensure identification accuracy. Additionally, this technology could be applied to surveillance cameras and access control systems that capture images from lateral or posterior angles. Additionally, leveraging the identified criticality of the helix region in this study, the development of identification algorithms that prioritize specific regions of the ear would also be a promising basis for future studies. This approach could contribute to the design of innovative identification methods that can function with partial or low-quality images. Furthermore, further studies on improving the identification accuracies of images captured from posterior angles could facilitate the development of more versatile and robust personal identification technologies. Notably, the following four limitations are acknowledged about this study:

1. First, equal and sufficient ear images captured from multiple directions are required for each data class.