# Lightweight Human Ear Recognition Based on Attention Mechanism and Feature Fusion
## 4 Experimental Results and Analysis
### Selection Strategy of Ear Recognition Method  
In this paper, based on YOLOv5s-MG, two improved ear recognition methods, YOLOv5s-MG-CBAM and YOLOv5s-MG-CBAM-F, were proposed from the perspective of improving accuracy based on the lightweight ear recognition method YOLOv5s-MG. The comparison results of the four methods are shown in Figure 11.  
It can be seen from Figure 11 that all the four methods can realize human ear recognition. Among the two improved methods, for the three distinctive human ear datasets, the YOLOv5s-MG-CBAM-F method has the best accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Human Ear** \\ **Dataset** \\ \end{tabular} } & \multirow{2}{*}{**Device**} & \multicolumn{6}{c}{**Model**} \\ \cline{3-8}  & & & **YOLOv5s-** & **YOLOv5s-** & **YOLOv5s-** & **YOLOv5s-** & **YOLOv5s-M** & **YOLOv5s-M** \\  & & & **MG-CBAM-F** & **MG-CA-F** & **MG-SE-F** & **G-ECA-F** & **YOLOv7** \\ \hline CCU-DE & CPU & 69 & 114 & 81.2 & 82.5 & 82.6 & 1000 \\ (epoch = 150) & GPU & 12.4 & 15.9 & 15 & 14.5 & 15.1 & 711.9 \\ \hline USTB & CPU & 117.6 & 170.2 & 126.9 & 128.5 & 128.5 & 769.2 \\ (epoch = 150) & GPU & 12.8 & 16.2 & 15.4 & 15.1 & 14.9 & 975.4 \\ \hline EarVN1.0 & CPU & 89.3 & 137.8 & 96.9 & 94.5 & 101.1 & 909.1 \\ (epoch = 150) & GPU & 12.8 & 16 & 14.8 & 14.3 & 14.3 & 803.5 \\ \hline EarVN1.0 & CPU & 87.7 & 137.6 & 117.4 & 103.4 & 113.4 & 769.2 \\ (epoch = 1000) & GPU & 12.6 & 16.4 & 15.2 & 14.8 & 14.7 & 807 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The inference time per image of the six methods on the testing set of three datasets. The unit is milliseconds (ms).  
It can be seen from Figure 11a that the same method has different ear recognition accuracy on different ear datasets, which is mainly related to three factors: the human ear dataset, feature extraction ability of the model and convergence of model training. The human ear dataset with high resolution, a small amount of data and simple posture change has a high ear recognition rate, that is, the [EMAIL_ADDRESS] value is large on the USTB human ear dataset, medium on the CCU-DE human ear dataset and small on the EarVN1.0 human ear dataset. Because the YOLOv5s-MG-CBAM-F method has the strongest feature extraction ability, the [EMAIL_ADDRESS] value is the largest on the EarVN1.0 human ear dataset. On the EarVN1.0 dataset, the [EMAIL_ADDRESS] value is low when the model does not converge at epoch = 150, and is high when the model converges at epoch = 1000.  
It can be seen from Figure 11b-d that the real-time performance of the improved methods YOLOv5s-MG-CBAM-F is worse than the method YOLOv5s-MG before improvement. However, because the real-time performance of the YOLOv5s-MG method is particularly good, the improved YOLOv5s-MG-CBAM-F method can still meet the real-time requirements.  
According to the theoretical analysis of Section 3 and the experimental results and analysis of Section 4.4, Section 4.5, Section 4.6, Section 4.7, the selection strategy of the human ear recognition method is formulated, that is, according to different human ear datasets, the principle of accuracy priority is adopted when the accuracy and real-time performance meet the requirements at the same time. For the three human ear datasets in this paper, the YOLOv5s-MG method is selected on the CCU-DE and USTB human ear datasets; on the EarVN1.0 dataset, the YOLOv5s-MG-CBAM-F method is selected.