# Lightweight Human Ear Recognition Based on Attention Mechanism and Feature Fusion
## 4 Experimental Results and Analysis
### Ear Recognition Experiments of the Improved YOLOv5s-MG-CBAM-F on Three Datasets
Figure 7: The [EMAIL_ADDRESS] value of YOLOv5s-MG-CBAM-F (proposed), YOLOv5s-MG (proposed), YOLOv5s-MG-SE-F, YOLOv5s-MG-CA-F, YOLOv5s-MG-ECA-F and YOLOv5s models trained on three human ear datasets. (**a**) CCU-DE human ear dataset; (**b**) USTB human ear dataset; (**c**) EarVN1.0 human ear dataset. From Table 2 and Figure 8a, on the USTB human ear dataset, YOLOv5s-MG-CBAM-F has the highest ear recognition rate in six methods and the [EMAIL_ADDRESS] value is 1. CCU-DE follows; [EMAIL_ADDRESS] is above 0.98 and the [EMAIL_ADDRESS] value of YOLOv5s-MG-CBAM-F was within \(\pm\)0.3% compared with other methods. Compared with the CCU-DE and USTB ear datasets, the EarVN1.0 dataset has the lowest ear recognition rate. However, on the EarVN1.0 dataset, when epoch = 1000, the [EMAIL_ADDRESS] value of YOLOv5s-MG-CBAM-F is 0.919. The human ear recognition rate is the highest among the six methods, which is 3.7% higher than YOLOv5s, 6.4% higher than YOLOv5s-MG, 6.3% higher than YOLOv5s-MG-SE-F, 4.1% higher than YOLOv5s-MG-ECA-F and 4.9% higher than YOLOv5s-MG-CA-F. However, the [EMAIL_ADDRESS] value of YOLOv5s-MG-SE-F, YOLOv5s-MG-ECA-F and YOLOv5s-MG-ECA-F is lower than that of YOLOv5s. From Table 2 and Figure 8b-d, we can see that the parameters, calculation and model size of the YOLOv5s-MG-CBAM-F method are lower than those of YOLOv5s, but higher than other methods. However, YOLOv5s-MG-CBAM-F method still has a lower params, GFLOPS and model size. On the CCU-DE human ear dataset, the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are 78.5%, 48.8% and 75.2% of YOLOv5s, respectively. On the USTB human ear dataset, the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are 85.4%, 52.1% and 86.3% of YOLOv5s, respectively. On the EarVN1.0 ear dataset, the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are 76.9%, 50.6% and 79.6% of YOLOv5s, respectively. Comparing the experimental results before and after feature fusion, it can be seen from Tables 1 and 2 that on the EarVN1.0 dataset, with the exception that the [EMAIL_ADDRESS] value of YOLOv5s-MG-SE-F is 0.3% lower than that of YOLOv5s-MG-SE, YOLOv5s-MG-CBAM-F, YOLOv5s-MG-ECA-F and YOLOv5s-MG-CA-F were 3.6%, 4.5% and 1.5% higher than YOLOv5s-MG-CBAM, YOLOv5s-MG-ECA and YOLOv5s-MG-CA, respectively. The experiment quantitatively proves that by adding the SPPF network and PANet network in the YOLOv5s-MG-CBAM with an attention mechanism, the feature extraction and feature fusion ability of the network are indeed increased, and the accuracy of human ear recognition is improved. At the same time, the params, GFLOPS and model size are also increased, but they are lower than the YOLOv5s ear recognition network. The quantitative ear recognition results of the six methods on testing the set of three datasets are shown in Figures 9 and 10 and Table 3.  
Figure 8: The comparison results of YOLOv5s-MG-CBAM-F (proposed), YOLOv5s-MG, YOLOv5s-MG-SE-F, YOLOv5s-MG-CA-F, YOLOv5s-MG-ECA-F and YOLOv5s models on three datasets. (**a**) [EMAIL_ADDRESS]; (**b**) the parameter quantity (params); (**c**) the calculation amount (GFLOPS); (**d**) the model size. Figure 10: The quantitative ear recognition results of the six methods on USTB datasets. (**a**) YOLOv5s-MG-CBAM-F (proposed); (**b**) YOLOv5s-MG-CA-F; (**c**) YOLOv5s-MG-ECA-F; (**d**) YOLOv5s-MG-SE-F; (**e**) YOLOv5s; (**f**) YOLOv5s-MG.  
Figure 9: The quantitative ear recognition results of the six methods on CCU-DE datasets. (**a**) YOLOv5s-MG-CBAM-F (proposed); (**b**) YOLOv5s-MG-CA-F; (**c**) YOLOv5s-MG-ECA-F; (**d**) YOLOv5s-MG-SE-F; (**e**) YOLOv5s-MG.  
the original image. Therefore, the method proposed in this paper is also suitable for ear recognition in some dynamic environment changes. Table 3 is the quantitative ear recognition results of the six methods on EarVN1.0 datasets. From Figure 6, we can see that on 10 types of human ears, the range of recognition results of the proposed YOLOv5s-MG-CBAM-F and YOLOv5s-MG method are 80-94% and 71-92%.