## 3 Results
### Demonstration Using a Driving Dataset  
Because our framework was designed to be employed under driving conditions, a real driving dataset was collected. This dataset included 19 subjects, both male and female, in their 20s to 30s. The test subjects were from the Middle East and Asia (Korean, Chinese, and Taiwanese), with some wearing glasses (Figure 4). \begin{table}
\begin{tabular}{c c c c c} \hline
**Feature** & \(\textbf{M}_{e}(SD_{e})\) & _RMSE_ & \(\textbf{M}_{\text{RRate}}\) & \(r\) \\ \hline _RoverG\({}_{\text{JBSS}+\text{EEMD}}\)_ & -3.36 (5.10) & 7.39 & 9.29\% & 0.10 \\ _RoverG\({}_{\text{excluded}}\)_ & **-1.22** (**4.16**) & 7.49 & 9.58\% & 0.06 \\ _RoverG\({}_{\text{NLMS}}\)_ & -3.36 (**3.33**) & **5.31** & **6.57\%** & **0.72** \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the performance for each stage of the proposed framework using the MAHNOB-HCI dataset (best performance in bold). RMSE—root-mean-square error; JBBS—joint blind source separation; EEMD—ensemble empirical mode decomposition; NLMS—normalized least mean squares. \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & \(\textbf{M}_{e}(SD_{e})\) & _RMSE_ & \(\textbf{M}_{\text{RRate}}\) & \(r\) \\ \hline _[PERSON]_ (2010) & -8.95 (24.3) & 25.9 & 25.0\% & 0.08 \\ _[PERSON]_ (2011) & 2.04 (13.5) & 13.6 & 13.2\% & 0.36 \\ _[PERSON]_ (2014) & -3.30 (6.88) & 7.62 & 6.87\% & 0.81 \\ _[PERSON]_ (2016) & 3.19 (5.81) & 6.23 & 5.93\% & **0.83** \\ _[PERSON]_ (2018) & **0.80** (3.35) & **3.26** & **3.68\%** & 0.75 \\ _[PERSON]_ & -3.36 (3.33) & 5.31 & 6.57\% & 0.72 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the performance of our proposed framework with other approaches using the MAHNOB-HCI dataset (best performance in bold). Figure 4: Examples of the appearance of the subjects used in the driving dataset: (**a**) Asian; (**b**) Asian with glasses; (**c**) Middle Eastern. The driving dataset was captured using a GoPro HERO3+, with the camera fixed on the windscreen looking at the subject's face. The video was recorded at a resolution of \(1920\times 1080\) and a frame rate of 30. The ground-truth was obtained using a contact-based pulse sensor (the MP507 model from MEK) attached to each subject's earlobe and synchronized with the captured video data. The subjects sat in the passenger seat to ensure their safety. In order to simulate the most common movements of real drivers, the subjects were asked to rotate their head and to look up, down, left, and right. In addition, in order to determine whether our method was able to accurately detect a change in the pulse while driving, the subjects were asked to run on a steep hill before entering the vehicle. In all driving situations, subjects were asked to freely make motions with no special restrictions, to set the environment as close as possible to the actual driving conditions. The driving course was subject to a lot of shadows, rapid illumination changes, and vibrations caused by the unevenness of the road surface. As stated in Appendix A, we collected driving dataset on research ethics. Table 3 presents the experimental results compared with recent methods for the driving dataset. The compared methods were selected based on the latest results and major algorithms, and these methods were re-implemented to obtain the results on the same driving dataset. [PERSON] (2011) and [PERSON] (2013) were some of the backbones of related research, but they did not show robustness in the driving dataset in dynamic conditions. Unlike the previous methods, [PERSON] (2017) showed significantly higher results in \(M_{e}\) but did not show strength in other metrics. On the other hand, [PERSON] (2018) showed an overall high performance compared with previous methods in several metrics. However, in terms of \(r\), there was a slight increase compared to the previous results, but still no reliable performance. On the other hand, our method demonstrated a slightly lower performance using the public dataset compared to recently published approaches.