## 4 Experimental Results
### Human Ear Datasets
#### 4.1.3 EarVN1.0  
EarVN1.0 [38] is a large-scale ear image dataset established by the Faculty of Computer Science of Ho Chi Minh City Open University in Vietnam in 2018. This is one of the largest public ear datasets in the research field, containing 28,412 color images of 164 participants, of which 98 men provided 17,571 male ear images and 66 women provided 10,841 female ear images.  
This dataset is different from the previous dataset. The original photo was taken under the condition that the camera system, illumination conditions were not limited, and the human ear image was cropped under the condition that the original image had great changes in posture, scale, and illumination. Each subject provided at least about 100 left or right ear images. The resolution of human ear images is also uneven, and some images have very low resolution (less than 25 \(\times\) 25 pixels).  
In this paper, we selected the first 15 kinds of human ear samples from the dataset of EarVN1.0 for experiments. There are 3201 photos. According to the ratio of training set: test set: verification set = 3:1:1, the training set has 1921 pictures, and the test set and verification set each have 640 pictures, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Category** & **Left Ear (NF)** & \begin{tabular}{c} **Right Ear** \\ **(NF)** \\ \end{tabular} & \
\begin{tabular}{c} **Total (NF)** \\ **Picture** \\ \end{tabular} &
\begin{tabular}{c} **Size of the** \\ **Picture** \\ \end{tabular} \\ \hline Eardata4 & ear1 & 153 & 135 & 288 \\ Eardata3 & ear25 & 111 & 139 & 250 \\ Eardata3 & ear26 & 112 & 154 & 266 \\ Eardata4 & ear27 & 249 & 156 & 405 \\ Eardata4 & ear28 & 96 & 106 & 202 \\ Eardata4 & ear29 & 163 & 188 & 351 & 1280 \(\times\) 720 \\ Eardata4 & ear30 & 156 & 124 & 280 \\ Eardata4 & ear31 & 203 & 186 & 389 \\ Eardata4 & ear32 & 175 & 182 & 357 \\ Eardata4 & ear33 & 235 & 251 & 486 \\ \hline total & & 1653 & 1621 & 3274 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The selection of CCU-DE human ear dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & & & & **Attitude Change** & & **Size** \\ \hline
70 & \(0^{\circ}\) & \(5^{\circ}\) & \(-5^{\circ}\) & \(10^{\circ}\) & \(-10^{\circ}\) & \(20^{\circ}\) & \(-20^{\circ}\) & flip left and right &
\begin{tabular}{c} enhancement \\ contrast brightness color \\ \end{tabular} & \(768\times 576\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The sample dataset of database3 in USTB.  
From Sections 4.1.1-4.1.3, we can see that the three ear datasets in this experiment have their own characteristics, as shown in Table 8.  
The above three datasets have different characteristics in this paper. Compared with the other two ear datasets, the CCU-DE ear dataset is dynamic, with the least number of categories, the smallest amount of data, rich attitude changes, and the highest image resolution. Compared with the other two ear datasets, the USTB ear dataset is static, with the largest number of categories, the largest amount of data, rich attitude changes, and medium image resolution. Compared with the other two ear datasets, the EarVN1.0 ear dataset is static, with fewer categories and fewer data, but with rich attitude changes, uneven image resolution, and poor parts.