## 4 Results  
For face identification, the results were extracted in the same manner as in LFW. The prediction of whether the ID of the two faces is the same or different is accurately expressed. Figure 10 shows the histograms for the two test situations, where the x-axis represents the cosine distance of the embedding vectors of the two face images. Blue bars indicate same-person comparisons, and red bars indicate different-person comparisons. It can be observed that the cosine distance is short for most same-person comparisons and long for most different-person comparisons, although there is some overlap. Interestingly, a significant difference was observed when the upper facial region was used. Specifically, the histograms in Figure 10c,d show a larger separation between the blue and red bars when using the upper facial region than when using the lower facial region. This suggests that  
Figure 9: Overall experimental setup of face identification. the upper facial region plays a more critical role in facial recognition than the lower facial region. Figure 10e,f show the receiver operating characteristics (ROC) curves for the two test situations. These graphs show that the upper facial region is more suitable for face identification. Figure 10: Accuracy of detailed facial identification by part of the face. Table 3 shows that the face recognition rate is much higher when using the upper facial region than when using the lower facial region. Accordingly, we can see why people are able to easily recognize one another during COVID-19, even while wearing masks. In the case of facial expression recognition, the results were contrary to those of facial recognition. The recognition rate was approximately 10% higher when the lower facial region was used than when the upper facial region was used. In other words, the accuracy of using the upper facial region was approximately 1,465 times that of using the lower facial region. To analyze the results in more detail, the recognition rate for each facial expression was extracted. Figure (a)a shows the recognition rate for each facial expression when using the upper facial region, and Figure (b)b shows the recognition rate for each facial expression when using the lower facial region. In the case of the two red-boxed facial expressions in Figure (a)a, it can be seen that they converge to almost zero. When the learned model recognizes only the upper facial region, it tends to fail to predict the facial expressions of happiness and contempt. Conversely, in the case of sadness and anger, it can be seen that the accuracy is higher when the upper facial region was used compared to the lower facial region. We can observe that a person is more conscious of the upper facial region when determining whether an emotion is sadness or anger. Because facial identification is a binary classification, and facial expression recognition is a problem in classifying eight classes, we assumed that a one-to-one comparison of the two fields would also be necessary. Performance was derived by defining sadness, the most representative facial expression, as a binary classification problem. The accuracy of  
Figure 11: Accuracy of detailed facial expression recognition by part of the face. Red boxed expressions represent expressions that can not be recognized with upper part of the face. \begin{table}
\begin{tabular}{c c c} \hline  & **Upper Parts** & **Lower Parts** \\ \hline Face identification & 81.36\% & 55.52\% \\ Facial Expression Recognition (8 classes) & 39\% & 49\% \\ Facial Expression Recognition (2 classes) & 51\% & 88\% \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy of face identification and facial expression recognition according to the upper and lower facial regions. the upper facial region was approximately 51.54%, whereas when using the lower facial region, it was 88.85%. This indicates that using the lower facial region had approximately 1.256 times better performance than using the upper facial region.