# DA-FER: Domain Adaptive Facial Expression Recognition
## 5 Conclusions  
Facial expression recognition (FER) is an essential field with many practical applications, but dealing with small sample data is a significant challenge. To improve the classification accuracy of FER under small sample data, we proposed a domain adaptive transfer learning method in this paper. Our approach involved using the public datasets RAF-DB and Fer2013 as the source domain, and a self-collected dataset as the target domain. We also applied the SSPP module for multi-dimensional feature fusion with variable perceptual fields and the slice module to retain regions of interest. Our optimized paradigm of transfer learning, mean module, and adaptive averaging pooling operation effectively reduced the number of model parameters, thereby achieving the best classification performance in terms of complexity and accuracy of the target domain. Experimental results showed that our proposed method outperformed the other seven popular networks. The domain adaptive method DA-FER, compared with the original DenseNet121 network,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Extraction** & **Number of** & **Number of** & **Inference Time** \\
**Network** & **Trainable Parameters** & **Parameters** & **Mini-100** \\ \hline AlexNet & 4,831,047 & 4.83 M & 6.69 s \\ DenseNet121 & 19,800,967 & 19.8 M & 5 s \\ Resnet-34 & 21,417,799 & 21.42 M & 6.82 s \\ Resnet-50 & 24,034,375 & 24.03 M & 5 s \\ VGG11 & 15,645,063 & 15.65 M & 5 s \\ VGG13 & 15,829,575 & 15.83 M & 7.47 s \\ VGG16 & 21,139,271 & 21.14 M & 7.36 s \\ DA-FER & 9,365,415 & 9.37 M & 5s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the number of parameters of each network.  
improved the accuracy by 2.54% when mini_batchsize was set to 200 and reached the highest value of 58.46% when mini_batchsize was set to 100, which was 4.95% higher than the original network.  
At present, we have not given enough consideration to the problem of network generalization, such as training on the source domain and predicting the target domain. In the future, we also intend to explore the effects of larger mini_batchsize settings (e.g., 300-600) on experimental outcomes and test more networks for feature extraction and additional datasets as source domain datasets. Through such efforts, we can enhance the generalizability of our experimental results and further expand our understanding of transfer learning and domain adaptive techniques.  
Conceptualization, M.B. and X.C.; methodology, H.X. and Q.L.; investigation, H.X. and Y.G.; writing--original draft preparation, M.B., K.S. and H.X. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China under Grant 62172184, Science and Technology Development Plan of Jilin Province of China under Grant 2020040107 GX, 20200201292 JC, Social Science Research of the Education Department of Jilin Province (JIKH20210901 SK), Jilin Educational Scientific Research Leading Group (ZD21003) and Humanities and Social Science Foundation of Changchun Normal University (2020[011]).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.