## 5 Results
To measure the impact of brightness and head rotation on the InceptionV3 with SGD model, we applied the model on the three adjusted testing data (unseen subject) and recorded the accuracy for each testing data. Table 6 shows the impact of illumination and head rotation of the dataset on the InceptionV3 with SGD model's accuracy, as compared to its 90.65% accuracy on the original testing data. As Table 5 shows, increasing the brightness dropped the accuracy by 00.81% while decreasing the brightness and the head rotation had no negative effect on the efficiency of the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & **Use All Images** & **Number of** & **Number of Classes** & **k-Fold Cross Validation** & **Accuracy of Unseen** \\  & **in Dataset** & **Used Images** & **Accuracy (\%)** & **Subject (\%)** \\ \hline [PERSON] et al. [30] & Yes & 40,029 No pain & 2 & 96.40 \\ [PERSON] et al. [32] & No & 8369 Pain & 2 & 83.00 \\ [PERSON] and [PERSON] [5] & No & 16,000 & 4 [0,1,(2 + 3),(4 + 5\({}^{\circ}\))] & 94.00 \\  & No + collected data of &
\begin{tabular}{c} UNBC-McMaster + \\ 16,460 from \\ collected data \\ \end{tabular} & 4 & 96.00 \\ [PERSON] and [PERSON] [34] & No & 4 & 91.87 \\ Proposed approach & No & 600 & 4 [0,1,2,3\({}^{\circ}\)] & 99.10 & 90.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with other approaches using UNBC-McMaster dataset. Figure 9: The convergence curves in InceptionV3 training with four optimizers. To test the response time of our system, we ran FEAPAS four times, i.e., twice for each frame selection approach using sequence length values (N = 30). The experiment was conducted by playing the test videos on a screen and directing the laptop's webcam to the screen to mimic a real live feed. A stopwatch was used to record the time, at which point the alarm was activated. We tested the following four scenarios with a sequence length of N = 30. The middle and boundary frame selection strategies with a segment length of 2\(\Delta\) are as follows:  
* One frame at each end of the sequence: two-boundary test frames: 2-B, \(\Delta=1\)
* Two frames at the middle of the sequence: two-middle test frames: 2-M, \(\Delta=1\)
* Two frames at each end of the sequence: four-boundary test frames: 4-B, \(\Delta=2\)
* Four frames at the middle of the sequence: four-middle test frames: 4-M, \(\Delta=2\)  
Table 7 shows the impact of frame selection approach in response time. The larger segment length required more frames to be classified, which thus led to longer response time. Finishing classification at the middle or at the end of the sequence was an important factor and should be considered in future studies. Moreover, 2-M-30 showed the lowest average response time of 6.49 s, and 4-B-30 showed the highest average response time of 29.86 s.  
Figure 10 shows the output of FEAPAS on video 1 by using the 2-B-30 selected frame strategy. The FEAPAS stored the captured frames and classified them as pain before saving the date and time with the pain level. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Accuracy (\%)** & **Performance (\%)** \\ \hline The brightened images & 89.75 & \(-\)00.81 \\ The dimmed images & 91.56 & +00.91 \\ The images with a slight rotation. & 91.17 & +00.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The impact of brightness and head rotation on the InceptionV3_SGD model. Figure 10: The output of FEAPAS on video 1 using the 2-B-30 strategy. Reprinted/adapted with permission from Ref. [10]. Copyright (@)[PERSON]). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **2-B-30** & **2-M-30** & **4-B-30** & **4-M-30** \\ \hline Video 1 & 32.97 & 12.34 & 40.38 & 15.93 \\ Video 2 & 03.33 & 02.95 & 06.70 & 12.45 \\ Video 3 & 07.05 & 06.09 & 07.76 & 06.11 \\ Video 4 & No alarm & No alarm & No alarm & No alarm \\ Video 5 & 48.31 & 06.89 & 51.96 & 07.11 \\ Video 6 & No alarm & No alarm & No alarm & No alarm \\ The average & 27.68 & 6.49 & 29.86 & 6.61 \\ \hline \hline \end{tabular}
\end{table}