## 2 Related Works  
### Facial Expression Recognition of Upper and Lower Facial Regions  
Several previous studies have analyzed the accuracy of facial expression recognition based on different regions of the face. A brief summary of these studies is presented in Table 1. One of the earliest studies was conducted by [PERSON] et al. [8]. They first selected two representative expressions: happiness and sadness; these can be seen as the two most opposing and representative expressions from the perspective of the valence coordinate axis, which is still widely used in facial expression recognition research. They then divided the level of happy and sad expressions into seven steps to share the degree of each expression. They took the same person's happy and sad images and morphed them into seven steps using SantaMorph 4.0 [9]. The test data consisted of the upper and lower facial regions of the generated image separated and randomly merged. Four people were used as data, and the experiment was conducted with 49 images per person. Subsequently, the results were derived from the standard normal and cumulative distribution functions through a survey. Only the upper and lower facial regions were observed to affect happiness [8]. In this study, we also compared the recognition rates for sadness and happiness in the upper and lower facial regions. Moreover, we used natural images of people, derived the results from a much larger dataset, and compared the results of eight different expressions. This clearly showed how the upper and lower facial regions affect facial expressions. [PERSON] at el. conducted an experiment using six emotional labels: anger, disgust, fear, happiness, sadness, and surprise, with natural expressions [10]. After setting the upper or lower facial region as neutral and the other face as one of the facial expression \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Summary** & **Limitations** \\ \hline [PERSON] et al. [8] & Using the FantaMorph 4.0 dataset, the sad and happy facial expressions were divided into & Lack of test data set and classifying only happy and sad emotions. \\  & 7 steps. & The lack of access to a diverse demographic pool \\ \hline [PERSON] at el. [10] & The six emotions were tested by changing only the upper part of the face, and the relationship & The lack of access to a diverse demographic pool \\  & between each emotion was analyzed. & based on the opinion of a female college \\  & & between each emotion was analyzed. \\ \hline [PERSON] et al. [11] & In the faces extracted from images and videos, & The characteristics of action units were classified \\  & the effect of Action Unit on upper and lower & for the entire face. Characteristics by region of \\  & facial expression classification was analyzed. & the face were not analyzed. \\ \hline [PERSON] et al. [12] & Using the CK+ and RAF-DB datasets, this study & Only the upper part of the face was used to \\  & generated a face wearing a mask and calculated & analyze the effect of the upper part. \\  & the accuracy through CNN. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of facial expression recognition related works. labels, the participants were asked to evaluate the intensity of the emotion. The upper facial region was highly related to anger, surprise, and sadness, whereas the lower facial region was related to fear and happiness. Evaluation of the upper facial region, which is relatively important in facial expression recognition, was not significantly different from that of the entire face. Anger and fear are often confused with disgust and surprise, respectively [10]. The conclusions of this study were based on the opinions of 63 female university students, and the results may not be representative of all people because of the lack of access to a diverse demographic pool; additionally, another limitation is that the data used in the experiment were small and self-generated. Therefore, we used a model trained on a large amount of data to obtain the results. [PERSON] et al.