## 4 Experimental Results
### Training Results Derived by Applying the DA-S Technique  
This study initially compared the performance of data augmentation techniques, including Flip, Grayscale, Brightness, and Saturation, to determine their effectiveness (Table 3). The differences in dataset size across techniques were attributed to the number of adjustable parameters available for each method. Notably, Flip and Grayscale lack adjustable parameters, which limits their performance enhancement. While the results suggest that dataset size differences may have influenced performance, further validation focusing on the parameter-specific impacts of Brightness and Saturation was deemed necessary. Among these techniques, Saturation demonstrated the highest mean accuracy of 91.68%. Consequently, additional experiments were conducted to analyze the parameter-specific effects of the Saturation technique. The DA-S technique was employed during model training to address overfitting. To compare model performance, different saturation weights (\(w\)) were applied. Specifically, the performance of the ResNet18 and ResNet34 models, which exhibited similar mean accuracies among ResNet models, was compared. Two conditions were considered in the experiments: one involving an increase in the number of data samples in a general DB and the other involving an increase in the number of samples in a DB augmented with the DA-S. These assumptions were necessary to determine a significant difference as a result of the application of data augmentation. Table 4 shows comparison results obtained in the following two different conditions: a condition where Original DB-T comprises 11,192 data samples (Original DB-A: 5596 data samples, Original DB-B: 5596 data samples) and a condition where the DB augmented with the DA-S technique comprises 11,192 data samples (Original DB-A: 5596 data samples, DA-S: 5596 data \begin{table}
\begin{tabular}{c c c c c c} \hline
**Data Augmentation** & **-** & **Flip** & **Grayscale** & **Saturation** & **Brightness** \\ \hline Number of data & 11,392 & 22,784 & 22,784 & 34,176 & 125,312 \\ Mean Accuracy & 53.02\% & 58.97\% & 81.02\% & 91.68\% & 82.82\% \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of emotion classification performance by data augmentation methods based on ResNet18 model. Figure 6: Comparison of performance among ResNet models through k-fold cross validation (k = 5) according to layer depth. (**a**) Mean accuracy of the ResNet18 model. (**b**) Mean accuracy of the ResNet34 model. (**c**) Mean accuracy of the ResNet50 model. samples). When Original DB-T was used, the ResNet18 and ResNet34 models exhibited mean accuracies of 56.96% and 57.02%, respectively. When a saturation weight of 0 was applied, the ResNet18 and ResNet34 models exhibited mean accuracies of 76.78% and 65.38%, respectively. When a saturation weight 2 was applied, the ResNet18 and ResNet34 models exhibited mean accuracies of 69.52% and 81.86%, respectively. When a saturation weight 3 was applied, the ResNet18 and ResNet34 models exhibited mean accuracies of 80.36% and 76.86%, respectively. The difference between mean accuracies calculated based on Original DB-T and those from the DB augmented with the DA-S technique ranged from 8.42% to 24.9%. Table 5 shows the mean accuracies of training results derived by a model before and after data augmentation across different DBs: Original DB-A with 5596 data samples, Original DB-T with 11,192 data samples, and the DB based on the DA-S technique containing 11,192 data samples (Original DB-A containing 5596 data samples and DA-S containing 5596 data samples). The mean accuracy calculated based on Original DB-A with 5596 data samples was 48.60%; that based on Original DB-T with 11,192 data samples was 56.96%; the mean accuracy based on DA-S DB with 11,192 data samples was 80.36%. Notably, the mean accuracy calculated based on the DB augmented with the DA-S technique where data were doubled was 23.4% higher than that calculated based on the non-augmented DB containing the same number of data samples.