## 4 Experiments
### Performance Analysis
#### 4.4.3 Performance for Dog Emotion Recognition  
The LDFMN model and the TestSet4_1 and TestSet4_2 data-sets were used for the emotion recognition experiments. In the experiment conducted using TestSet4_1, 16 images were selected as prediction targets, and the ResNet50 model incorporated into the LDFMN model was trained using ImageNet parameters. In the experiment conducted using TestSet4_2, 16 images were selected as prediction targets, and the Mask R-CNN and ResNet50 models incorporated into the LDFMN model were both trained using ImageNet parameters.  
The results of the emotion recognition experiments are presented in Table 15. In the experiments conducted using the TestSet4_1 data-set, the average identification accuracy of the LDFMN model was 81.73%, which is higher than that obtained using Convolutional 3D (C3D) [58] architecture (71.07%). The identification accuracy for anger/aggression (96.77%) was the highest among those for the three emotions. In the experiments conducted using the TestSet4_2 data-set, the average identification accuracy of the LDFMN model was 76.02%, higher than that obtained using C3D architecture (66.84%). Again, the identification accuracy for anger/aggression (88.70%) was the highest. The identification accuracy achieved using the TestSet4_1 data-set was higher than that achieved using the TestSet4_2 data-set, indicating that background removal did not contribute to higher dog emotion recognition. However, the identification accuracy for happiness achieved using the TestSet4_2 data-set was higher than that achieved using the TestSet4_1 data-set, indicating that background removal may be conducive to the recognition of happiness in dogs. Nevertheless, as illustrated in Figure 21, background removal can cause the loss of a dog's features, resulting in dog emotion recognition errors.  
Figure 19: Dog with ID 2 not tracked from image 266 to 274. (**a**) Image 265; (**b**) Image 266; (**c**) Image 274.  
Figure 20: Dog with ID 4 not tracked in images 302 and 303. (**a**) Image 302; (**b**) Image 303; (**c**) Image 304.  
The reasons for emotion recognition errors, illustrated in Table 16, can be classified into the following four cases:  
Case 1: An angry or aggressive dog is categorized as being happy or excited. For example, in the image in Table 15, the dog's mouth is only slightly open, and the dog's movements are too subtle.  
Case 2: The shooting angle is suboptimal.  
Case 3: The dog moves too quickly, resulting in blurry images.  
Case 4: The resolution of the image is too low.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{Dataset} & \multirow{2}{*}{Methods} & \multicolumn{3}{c}{Acc of the Emotion} & Average ACC \\ \cline{3-5}  & & **Emotion Type** & ACC & \\ \hline \multirow{6}{*}{TestSet4\_1} & \multirow{2}{*}{LDFMN} & Neutral/General & 77.65\% & \multirow{6}{*}{81.73\%} \\ \cline{3-5}  & & Happy/Excited & 70.00\% & \\ \cline{3-5}  & & Angry/Aggressive & 96.77\% & \\ \cline{3-5}  & & Neutral/General & 74.11\% & \\ \cline{3-5}  & & Happy/Excited & 66.00\% & 71.07\% \\ \cline{3-5}  & & Angry/Aggressive & 70.96\% & \\ \hline \multirow{6}{*}{TestSet4\_2} & \multirow{2}{*}{LDFMN} & Neutral/General & 66.66\% & \multirow{6}{*}{76.02\%} \\ \cline{3-5}  & & Happy/Excited & 76.00\% & \\ \cline{3-5}  & & Angry/Aggressive & 88.70\% & \\ \cline{3-5}  & & Neutral/General & 68.51\% & \\ \cline{3-5}  & & Happy/Excited & 68.00\% & 66.84\% \\ \cline{3-5}  & & Angry/Aggressive & 64.52\% & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Results of dog emotion recognition experiments.  
Figure 21: Images before and after background removal. (**a**) Original image; (**b**) After background removal.