## 4 Experiments and Results
### Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample Length** & **Filtering** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **FRR (\%)** & **FAR (\%)** \\ \hline
2000 & SGF, BWPF & 13.71 & 41.18 & 13.71 & 18.09 & 4.01 & 86.29 \\ \hline
2000 & SGF & 16.48 & 50.81 & 16.48 & 21.70 & 2.26 & 83.52 \\ \hline
2000 & BWPF & 55.65 & 56.41 & 55.65 & 58.30 & 0.75 & 44.35 \\ \hline
3000 & SGF, BWPF & 13.05 & 45.41 & 13.05 & 16.27 & 3.84 & 86.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results on the effects of noise reduction during training and validation using data from different sessions. Figure 10: Training processes of pretrained ResNet-101 with data augmentation when training and validating with data from different sessions. Figure 9: Training processes of pretrained ResNet-101 when training and validating with data from different sessions. Table 9 presents the experimental results of the ensemble deep neural network trained and validated using data from different sessions. By combining the output scores of individual deep neural networks through addition and multiplication operations, we designed Ensemble Deep Network-Plus (EDNet-P) and Ensemble Deep Network-Multiplication (EDNet-M). The EDNet-P and -M are ensemble deep neural networks that combine the outputs of three pretrained models: ResNet, ResNet with augmentation, and Inception-ResNet-v2, using addition and multiplication, respectively. In terms of accuracy, the proposed EDNets showed at least a 2.57% improvement compared with the single models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample Length** & **Method** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **FRR (\%)** & **FAR (\%)** \\ \hline
2000 & ResNet-101 & 55.08 & 54.23 & 55.08 & 59.13 & 0.77 & 44.92 \\ \hline
2000 & ResNet-101(aug) & 56.22 & 56.35 & 56.22 & 59.50 & 0.73 & 43.78 \\ \hline
2000 & Inception-ResNet-v2 & 54.67 & 56.55 & 54.67 & 57.15 & 0.78 & 45.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results on various single pretrained models when training and validating using data from different sessions. Figure 11: Training processes of pretrained Inception-ResNet-v2 when training and validating with data from different sessions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample Length** & **Method** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score** (\%)** & **FRR (\%)** & **FAR (\%)** \\ \hline
2000 & EDNet-P & 58.79 & 58.71 & 58.79 & 61.26 & 0.66 & 41.21 \\ \hline
2000 & EDNet-M & 59.11 & 60.05 & 59.11 & 60.73 & 0.65 & 40.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental results of ensemble deep neural network when training and validating with data from different sessions. Table 10 shows the experimental results of various network combinations when training and validating with data from different sessions. The reason performance can be improved is due to the complementary interactions between the networks' final scores. For example, suppose Net-A accurately classifies people 1 through 10 but poorly classifies people 11 through 20, while Net-B poorly classifies people 1 through 10 but accurately classifies people 11 through 20. Although each network might show similar poor performance individually, if Net-A and Net-B interact appropriately, they could collectively classify people 1 through 20 more effectively. From the perspective of the networks' output scores, even if Net-B assigns a relatively high score to the 5 th person, who is actually the 1 st person, Net-A is likely to assign a lower score to the 5 th person. Therefore, combining their scores would reduce the likelihood of classifying the 1 st person as the 5 th person. The reason why the performance of some network combinations does not improve significantly is that these combinations fail to complement each other's relative weaknesses. This study explores individual network characteristics through experimental repetition.