# AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
## 5 Conclusions  
In this paper, we address the unsupervised domain-adaptive facial expression recognition task with auxiliary facial action units. Our method is very different from existing cross-domain FER methods, which typically follow generic cross-domain methods. Specifically, we proposed an AU-guided unsupervised domain-adaptive FER (AdaFER) framework, which includes an AU-guided annotation module and an AU-guided triplet training module. We evaluated several AU-guided annotation strategies and triplet selection methods. Extensive experiments on several popular benchmarks have shown the effectiveness of our AdaFER.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Source Dataset** & **Backbones** & **CK+** & **JAFFE** & **FER2013** & **ExpW** & **Mean** \\ \hline [PERSON] et al. [58] & BOSPHORUS & HOG and Gabor Filters & 57.60 & 36.2 & - & - & - \\ [PERSON] et al. [59] & MMI and FERA and DISFA & Inception-ResNet & 67.52 & - & - & - & - \\ [PERSON] et al. [60] & MMI and FERA & Inception-ResNet & 73.91 & - & - & - & - \\ [PERSON] et al. [56] & Six Datasets & VGG-Net & 88.58 & 44.32 & - & - & - \\ [PERSON] et al. [61] & Six Datasets & Inception & 64.20 & - & 34.00 & - & - \\ DFN [62] & RAF-DB & Manually Designed Net & 78.83 & 57.75 & 52.37 & - & - \\ ECAN [42] & RAF-DB 2.0 & VGG-Net & 86.49 & 61.94 & 58.21 & - & - \\ \hline CADA [63] & RAF-DB & ResNet-18 & 73.64 & 55.40 & 54.71 & 63.74 & 61.87 \\ SAFN [64] & RAF-DB & ResNet-18 & 68.99 & 49.30 & 53.31 & 68.32 & 59.98 \\ SWD [65] & RAF-DB & ResNet-18 & 72.09 & 53.52 & 53.70 & 65.85 & 61.29 \\ LPL [11] & RAF-DB & ResNet-18 & 72.87 & 59.39 & 56.61 & 68.35 & 62.20 \\ DFN [62] & RAF-DB & ResNet-18 & 64.19 & 52.11 & 42.01 & 43.92 & 50.55 \\ ECAN [42] & RAF-DB & ResNet-18 & 66.51 & 52.11 & 50.76 & 48.73 & 54.52 \\ AGRA [17] & RAF-DB & ResNet-18 & 77.52 & 61.03 & 54.94 & 69.70 & 65.79 \\
**AdAFER** & RAF-DB & ResNet-18 & **81.40** & **61.37** & **57.29** & **70.86** & **67.73** \\ Baseline (\%) & FERPlus & ResNet-18 & 64.34 & 41.86 & - & 66.64 & 57.87 \\
**AdAFER** & FERPlus & ResNet-18 & 65.12 & 46.51 & - & 73.58 & 61.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the state-of-the-art methods on the CK+, JAFFE, FER2013, and ExpW datasets. The results of upper part are taken from the corresponding papers, and the results of the bottom part are taken from the implementation of a cross-domain facial expression recognition benchmark [57]. The last column shows the mean accuracy of the performances on all the datasets.  
Figure 7: Visualization of the feature distributions on the CK+ and FERPlus datasets. The top and bottom parts show the feature distributions using the baseline method and AdaFER, respectively.  
**Limitations and future work**. Since our method resorts to AU for consistent labeling, the accuracy of AU detection may be limited. In addition, our method actually only focuses on the label bias of different datasets; thus, the data bias problem in both FER and AU detection can be our future work.  
Conceptualization, X.P.; methodology, X.P. and Y.G.; software, Y.G. and P.Z.; data curation,Y.G. and P.Z.; writing--original draft preparation, Y.G.; writing--review and editing, X.P.;supervision, X.P. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China (62176165) and the Stable Support Projects for Shenzhen Higher Education Institutions (SZWD2021011).  
Not applicable.  
The authors declare no conflict of interest.