# Facial Emotion Recognition Analysis Based on Age-Biased Data
## 3 Results and Discussion
### Comparison of the Emotional Accuracy in the Age-Specific Learning Model  
Figure 8 represents the recognition accuracy for each emotion using SE-ResNeXt50 (32 \(\times\) 4 d). The average emotion recognition accuracy of SE-ResNeXt50 (32 \(\times\) 4 d) was 77.26 for adults and 81.57% for kids. In the results for the emotion recognition accuracy for adults, angry (67.35%), fear (66.80%), neutral (76.35%), and sad (66.47%) had lower than  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Dataset**} & \multicolumn{6}{c}{**Accuracy (\%)**} \\ \cline{3-8}  & & **Angry** & **Fear** & **Happy** & **Neutral** & **Sad** & **Surprise** & **AVG** \\ \hline \multirow{8}{*}{MobileNet-V2} & A/A & 81.17 & 42.71 & 77.83 & 72.30 & 55.43 & 90.74 & 70.03 \\  & A/K & 49.18 & 34.71 & 51.25 & 47.01 & 25.44 & 46.57 & 42.36 \\  & K/A & 49.83 & 34.86 & 47.10 & 58.52 & 34.98 & 46.58 & 45.31 \\  & K/K & 80.23 & 75.11 & 74.07 & 76.95 & 54.96 & 71.23 & 72.09 \\  & M/A & 50.53 & 32.73 & 73.29 & 61.36 & 45.12 & 74.75 & 56.30 \\  & M/K & 62.11 & 34.30 & 60.89 & 62.79 & 49.14 & 63.24 & 55.41 \\ \hline \multirow{8}{*}{SE-ResNeXt50 (32 \(\times\) 4 d)} & A/A & 67.35 & 66.80 & 91.47 & 76.35 & 66.47 & 95.15 & 77.26 \\  & A/K & 45.60 & 21.47 & 54.76 & 53.23 & 35.92 & 72.02 & 47.17 \\ \cline{1-1}  & K/A & 44.18 & 10.44 & 81.63 & 42.43 & 45.61 & 65.70 & 48.33 \\ \cline{1-1}  & K/K & 86.81 & 62.52 & 89.36 & 87.74 & 82.19 & 80.82 & 81.57 \\ \cline{1-1}  & M/A & 72.02 & 52.18 & 59.73 & 54.11 & 47.58 & 68.15 & 58.96 \\ \cline{1-1}  & M/K & 76.84 & 39.73 & 75.23 & 60.35 & 58.02 & 68.64 & 63.13 \\ \hline \multirow{8}{*}{ResNeXt101 (64 \(\times\) 4 d)} & A/A & 58.80 & 55.60 & 91.00 & 78.10 & 62.30 & 88.10 & 72.32 \\ \cline{1-1}  & A/K & 54.83 & 20.60 & 55.19 & 50.88 & 40.27 & 52.62 & 45.73 \\ \cline{1-1}  & K/A & 37.65 & 7.81 & 89.69 & 59.15 & 32.30 & 53.45 & 46.67 \\ [PERSON]cline{1-1}  & K/K & 85.13 & 38.14 & 95.36 & 84.21 & 51.43 & 79.01 & 72.21 \\ \cline{1-1}  & M/A & 49.39 & 33.16 & 78.72 & 45.16 & 55.83 & 75.16 & 56.24 \\ \cline{1-1}  & M/K & 69.73 & 23.70 & 69.46 & 52.29 & 58.32 & 77.21 & 58.45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition accuracy of each emotion for all [PERSON].  
Figure 7: ResNeXt101 (64 \(\times\) 4 d)’s accuracy according to different emotions: (**a**) angry, (**b**) fear, (**c**) happy, (**d**) neutral, (**e**) sad, (**f**) surprise.  
average accuracy. The above results are similar to the results of a previous study by [PERSON] et al. (2011). On the other hand, in the case of kids, fear (62.62%) and surprise (80.82%) were lower than the average. This shows that adults had difficulties in recognizing anger, fear, and sadness when these emotions were expressed consistently. On the other hand, kids, in contrast to adults, did not have low accuracy when recognizing angry emotions but obtained low accuracy with fear and surprise emotions. In order to solve this problem, this study showed the need to not only separate data by age but also to segment specific emotions in a specific age group or to contribute more to data processing.  
Table 5 summarizes the recognition accuracy of existing reports of the FER2013. The previous best model for single-network accuracy is 72.7% ([PERSON] et al., 2016). In this work, our model achieved an accuracy of 79.42%. This result shows that the accuracy of the separate dataset of adults and kids is higher than that of the existing dataset. The reason is that there is a difference in facial expressions in the emotional expression of adults and kids, so the existing dataset learns by classifying images of different facial expressions as the same emotion.