# DA-FER: Domain Adaptive Facial Expression Recognition
## 4 Experiment
### Results
As shown in Figures 8 and 9, from the perspective of AUC (area under the curve), the model classification ability of the DA-FER_100 is the same as that of the original Transfer_Dense network. However, compared with mini_100, the classification result of DA-FER_200 is higher, and the AUC of the model reaches the highest value of 0.8. Since the area under the curve is calculated based on all possible truncation values while considering the classification ability of the classifier for each sample, it is more robust, indicating the effectiveness of the DAF-FER. The accuracy of DA-FER_200 is 56.15%, which \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Network** & **Baseline-None** & **Baseline-True** & \begin{tabular}{c} **Source-RAF-DB** \\ **Mini\_100** \\ \end{tabular} & \begin{tabular}{c} **Source-Fer2013** \\ **Mini\_100** \\ \end{tabular} & \
\begin{tabular}{c} **Source-** \\ **RAF-DB** \\ **Mini\_200** \\ \end{tabular} &
\begin{tabular}{c} **Source-** \\ **Fer2013** \\ **Mini\_200** \\ \end{tabular} \\ \hline DenseNet121 & 0.63 & **0.66** & **0.77** & 0.69 & 0.71 & 0.73 \\ Resnet-34 & **0.66** & 0.64 & 0.76 & 0.73 & 0.77 & 0.70 \\ DA-FER & & & 0.76 & **0.76** & **0.80** & **0.76** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the area under the curve (AUC). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Network** & \begin{tabular}{c} **Baseline-None** \\ **None** \\ \end{tabular} & \begin{tabular}{c} **Baseline-True** \\ **True** \\ \end{tabular} & \begin{tabular}{c} **Source-RAF-DB** \\ **Mini\_100** \\ \end{tabular} & \
\begin{tabular}{c} **Source-Fer2013** \\ **Mini\_100** \\ \end{tabular} &
\begin{tabular}{c} **Source-** \\ **RAF-DB** \\ **Mini\_200** \\ \end{tabular} \\ \hline AlexNet & 18.75 & 18.75 & 41.15 & 43.07 & 41.15 & 45.00 \\ DenseNet121 & **48.82** & **53.51** & 52.69 & 48.46 & 52.69 & 49.23 \\ Resnet-34 & 47.65 & 50.00 & 53.85 & **51.92** & 51.92 & 50.00 \\ Resnet-50 & 35.54 & 39.45 & 49.62 & 49.62 & 49.23 & 50.38 \\ VGG11 & 42.57 & 40.62 & 40 & 48.85 & 40.38 & 46.92 \\ VGG13 & 18.75 & 18.75 & 40.77 & 46.54 & 40.77 & 46.54 \\ VGG16 & 18.75 & 19.14 & 43.46 & 48.07 & 42.31 & 46.92 \\ DA-FER & - & - & **58.46** & 51.54 & **56.15** & **50.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of source domain RAF-DB and source domain Fer2013. Figure 10: The confusion matrix on DA-FER. Figure 9: ROC curve of DA-FER_200. As can be seen from the experimental results shown in Figure 10a-d, the recognition results of anger, fear and disgust are generally low. In addition, from some misclassified samples, we observed that some faces exhibited excessive head rotation angles, leading to difficulties in recognition by the network. Another issue is the intra-class variability of expressions [45]. Some expressions have large amplitudes, making them easy to distinguish, while others have less prominent features, leading to confusion with other categories. [PERSON] et al. [46] also discussed the issue of confidence in expression samples, which explains why anger and sadness classification are considered easy in some studies [47]. In contrast, in our research, they fall into the more challenging category. Additionally, the complexity of these emotions and the limited availability of high-quality training data for these specific emotions may also lead to these challenges. The accuracy of DA-FER_100 in these three difficult categories has been significantly improved, compared with the original Transfer_DenseNet121, angry (+3.2%), disgust (+17.1%), fear (+14.7%) (see Figure 10a,d). From Figure 10e,f, it can be observed that there is no change in the accuracy of the happy classification when comparing DA-FER_200 and DA-FER_100; the accuracy of the sad (+22.6%) and surprise (+5.5%) classifications has improved, while the accuracy of the angry (\(-9.6\%\)), disgust (\(-2.8\%\)), fear (\(-8.8\%\)), and neutral (\(-16.6\%\)) classifications have decreased.