## 6 Conclusions  
In this study, the upper and lower facial regions were divided, and the identification and expression recognition of the face included in each region were investigated. The LFW and AffectNet datasets were preprocessed through face alignment. Face identification was performed using ResNet50 and ArcFace models, and facial expression recognition was performed using EmoNet. In the case of face identification, the accuracy was 81.36% when only the upper facial region was used and 55.52% when only the lower region was used. This proves that there are no problems in distinguishing a person from a face when wearing a mask. However, in facial expression recognition, an accuracy of 39% was derived when only the upper part was used and 49% when only the lower part was used; in facial expression recognition, the lower facial region is more advantageous than the upper part. When the recognition rate of each of the eight facial expressions was derived, happiness and contempt could not be distinguished solely by the upper facial region. Although previous studies classified the results of facial expressions into eight classes, it was thought that the results of the binary classification of facial expressions into positive and negative facial expressions were also necessary for the basic emotional development of children. Therefore, we performed binary classification with two expressions--happy, the most representative positive expression, and sad, the contrasting expression--and derived the corresponding performance. Consequently, 88.85 percent accuracy was obtained when only the lower portion was used, whereas 51.54 percent accuracy was obtained when only the upper portion was used. As mentioned earlier, the results of this study can be seen as a technological underpinning for identifying trends in society, as deep learning models focus on the human-like aspects of facial identification and facial expression recognition. Therefore, based on this result, we can assume that children who encounter a mask-wearing person are confused about the distinction between the negative and positive expressions on their face, suggesting grounds that can affect emotional development. Further research can be conducted on this topic, such as analyzing in detail which parts of the face are most affected by each facial expression, and a facial expression recognition model with attention that focuses on the parts of the face that contain the least amount of human identity information.  
Since our study only distinguished between upper and lower faces, it can be seen that we used a broader subset than action units. This will be the subject of future research. Additionally, we tested with cropped faces in a model trained on the whole face. Whilethis explains the initial situation of the COVID-19 pandemic, it is possible to extend the study by fine-tuning the model with cropped faces in the case of a prolonged pandemic, or by comparing the results of the model trained on new data. This will be the subject of our future work.  
Conceptualization, E.C.L.; methodology, S.K., B.S.A. and E.C.L.; validation, S.K. and B.S.A.; investigation, S.K. and E.C.L.; data curation, B.S.A.; writing--original draft preparation, S.K. and B.S.A.; writing--review and editing, E.C.L.; visualization, S.K. and B.S.A.; supervision, E.C.L.; project administration, E.C.L.; funding acquisition, E.C.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a 2022 Research Grant from Sangmyung University.  
Not applicable.  
Since the data used in this study is a public open dataset, it can be used by contacting the data holder.  
The authors declare no conflict of interest.