## 5 Discussion  
The results of this study can be analyzed from two perspectives. From the perspective of facial identification, the overall performance of the upper facial region was approximately 1.465 times that of the lower facial region. The cosine distance histograms of the embedding vectors for the same person and for different people are shown in Figure 10c,d, respectively. These graphs show that the largest differences occur when the embedding vectors are from different people. The red histogram shows that the highest amplitude when using the upper face appears at 0.8 and shows a magnitude of approximately 4000. In addition, it can be confirmed that most of them are distributed over 0.5. However, as shown in Figure 10b,d, when using the lower face, the embedding vectors from different people are also most heavily skewed towards zero. Thus, we can conclude that using only the upper facial region is more advantageous for facial identification than using only the lower facial region. In fact, during the 2020 COVID-19 pandemic, most people were able to recognize each other despite wearing masks, and various studies have shown that face identification is possible even if a mask covers the lower facial region. This also applies to Face ID on Apple devices, which shows that the most important information for face identification is distributed in the upper facial region. As mentioned in previous related studies, facial expressions have a common face shape. These are represented by action units: when most people are happy, their cheeks rise and the corners of their mouths turn up. When people are angry, their eyebrows are furrowed and their lips are pursed [31]. Thus, we can understand a person's expressions through several action units that appear on their face. In the cases of contempt and happiness, because the action unit identified from the mouth is larger, it cannot be observed from the upper facial region but only from the lower part. In contrast, in the cases of sadness and anger, prominent features such as squinting or watering eyes are more easily observed. Therefore, Figure 11 shows that in the cases of sadness and anger, the performance was better when the upper facial region was used than when the lower facial region was used. Several emotions exist for each facial expression where the lower facial region is advantageous and others where the upper part is advantageous. However, in our society, it is important to quickly understand whether an expression is positive or negative, such as happy or sad, to make interpersonal connections. Therefore, we performed a binary classification for happiness, which represents positive emotions, and sadness, which represents negative emotions. Figure 12: Accuracy of detailed facial expression recognition in binary classification. Interestingly, the results were 51% accurate when the upper facial region was used and 88% accurate when the lower facial region was used. This can be considered as the opposite of the difference in degrees, which is similar to the value of face identification. From this, we can see that the upper facial region contains a large amount of information related to human identification; in contrast, the lower facial region contains a large amount of information about facial expressions, with a significant difference. In other words, owing to the bias of facial expression information, children are confused when judging the emotions of another person wearing a mask. As mentioned in the introduction, we assumed that our deep learning models were representative of a diverse sample of people. It is true that we did not train the models with data labeled by children, which can make it difficult to draw a clear correlation with the social development of children. However, as with deep learning models, children learn from the socially constructed rules and labeled data of adults. This suggests that deep learning models and young children go through a similar learning process.