# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 4 Distributions of the Training and Testing Data  
As already mentioned, the ear-image data were collected using the continuous-shooting function of the camera. Thus, the differences between the features of consecutive ear images could be extremely small. Figure 3 shows a heatmap depicting the strength  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & \begin{tabular}{c} **Number of** \\ **Input Nodes** \\ \end{tabular}  & \begin{tabular}{c} **Number of** \\ **Output Nodes** \\ \end{tabular}  & \begin{tabular}{c} **Kernel Size** \\ **Function** \\ \end{tabular}  & \ \begin{tabular}{c} **Activation** \\ **Function** \\ \end{tabular}  \\ \hline Convolution 1 & \(96^{2}\) & \(64\times 94^{2}\) & \(3\times 3\) & \begin{tabular}{c} Rectified linear \\ unit (ReLU) \\ \end{tabular}  \\ Convolution 2 & \(64\times 94^{2}\) & \(64\times 92^{2}\) & \(3\times 3\) & ReLU \\ Pooling 1 & \(64\times 92^{2}\) & \(64\times 46^{2}\) & \(2\times 2\) & - \\ Convolution 3 & \(128\times 46^{2}\) & \(128\times 44^{2}\) & \(3\times 3\) & ReLU \\ Convolution 4 & \(128\times 44^{2}\) & \(128\times 42^{2}\) & \(3\times 3\) & ReLU \\ Pooling 2 & \(128\times 42^{2}\) & \(128\times 2^{2}\) & \(2\times 2\) & - \\ Convolution 5 & \(256\times 21^{2}\) & \(256\times 19^{2}\) & \(3\times 3\) & ReLU \\ Convolution 6 & \(256\times 19^{2}\) & \(256\times 17^{2}\) & \(3\times 3\) & ReLU \\ Convolution 7 & \(256\times 17^{2}\) & \(256\times 15^{2}\)of the differences between the central ear image (_h_) and the other images (_a-g,i-o_) of the 15 consecutive ear-image frames of one subject. The differences between the two images were detected using DiffIMG [40]. For example, in this figure, image _a-h_ represents the difference between images \(a\) and \(h\), with the differences overlaid on image \(h\). In the heatmap image, the yellow and red colors indicate strong and weak differences, respectively. As shown in Figure 3, the heatmap images closer to image \(h\) exhibited a scattered distribution of the yellow and red colors. Contrarily, the heatmap images farther from image \(h\) displayed a more distinct contrast between the yellow and red colors, indicating the evident differences of these images from \(h\). Notably, this trend was consistently observed in the other ear images collected. Further, introducing variations in the training data was crucial to enhancing the model performance. Based on these findings, we employed the following data-selection procedures to ensure that highly similar ear images were not included in the training and test datasets:  
1. We selected the ear images that were captured from each subject in chronological order by skipping every 14 images (e.g., the 15 th, 30 th, and 45 th images) and designated them as the test data. 2. We removed the ear images that were selected in Step 1 and their four adjacent ear images, two preceding and two following the images in chronological order. 3. We used the images that were neither selected nor removed in Steps 1 and 2 as the training data. 4. We randomly selected 10% of the ear images from the training data and designated them as the validation data. Notably, the ratio of the numbers of images in the training, testing, and validation data was 8:1:1, as obtained using the above procedure. Algorithm 1 presents the pseudocode for generating training, test, and validation data from ear images arranged in chronological order. ```
Input : PicsList : List storing ear images in chronological order Output : TrainPicsList : List for storing training images TestPicsList : List for storing testing images ValPicsList : List for storing validation images
1: main
2:j - LEN(PicsList) #Obtain the length of PicsList, i.e., the number of ear images stored in PicsList
3:for i -1 to j do
4:if (i % 15 = 0) then
5:ADD(PicsList[i], TestPicsList) #Add PicsList[j] to TestPicsList

Figure 3: Visualization of the differences between adjacent images.