## 3 Method
### Data Preprocessing  
The facial images obtained through the camera contained not only the face but also the clothing and background. To isolate only the face, a single-shot multiple detector (SSD) face detector was used to detect the faces in the images. However, the detected face regions still included the eyebrows, hair, and other elements. To remove these unwanted components, the RGB color space of the image was converted to the YCbCr color space, and only the skin region was selected. The selected range for the skin region was \(0\leq\) Y \(\leq\) 235, 77 \(\leq\) Cb \(\leq\) 127 and 133 \(\leq\) Cr \(\leq\) 173 [20]. After this process, the selected skin region was in the RGB color space. In this paper, the selected skin region of interest (ROI) used a time series of 10 channels (R, G, B, Y, Cr, Cg, Cb, H, S, and V) as the data. Therefore, the RGB signals were transformed into the YCrCgCb and HSV signals. To do this, the average values of R, G, and B per frame were calculated within the selected ROI. These values were then normalized to the range from 0 to 1 by dividing them by the maximum pixel value of 255. We denoted the normalized R, G, and B signals as \begin{table}
\begin{tabular}{c c c c c c} \hline
**ID** & **Gender** & **Age** & **Nationality** & **Cb Channel** & **Cr Channel** \\  & & & & **Average** & **Average** \\ \hline
1 & Male & 25 & Korean & 116.0 & 145.6 \\ \hline
2 & Female & 25 & Korean & 115.0 & 143.5 \\ \hline
3 & Male & 27 & Korean & 118.0 & 143.3 \\ \hline
4 & Male & 27 & Korean & 117.4 & 142.8 \\ \hline
5 & Female & 26 & Korean & 116.9 & 142.7 \\ \hline
6 & Female & 30 & Korean & 117.3 & 142.8 \\ \hline
7 & Male & 28 & Korean & 117.5 & 143.1 \\ \hline
8 & Female & 24 & Korean & 117.6 & 144.0 \\ \hline
9 & Female & 24 & Korean & 117.6 & 143.6 \\ \hline
10 & Female & 25 & Korean & 120.1 & 140.6 \\ \hline
11 & Male & 26 & Korean & 118.0 & 142.5 \\ \hline
12 & Male & 28 & Korean & 114.9 & 143.9 \\ \hline
13 & Male & 27 & Korean & 115.0 & 144.4 \\ \hline
14 & Male & 32 & Korean & 114.0 & 146.3 \\ \hline \end{tabular}
\end{table} 
Table 2: Demographic characteristics and the average values of the Cg and Cb color channels for the participants. \(\mathrm{R}^{\prime}\), \(\mathrm{G}^{\prime}\), and \(\mathrm{B}^{\prime}\), respectively. Using Equations (3)-(6) [21] and Equations (7)-(12) [22], the RGB signals could be transformed into the YCrCgCb and HSV signals, respectively. \[Y=16+\left(65.481\times R^{\prime}\right)+\left(128.533\times G^{\prime} \right)+\left(24.966\times B^{\prime}\right) \tag{3}\]  
\[Cr=128+\left(112\times R^{\prime}\right)+\left(-93.786\times G^{\prime} \right)+\left(-18.214\times B^{\prime}\right) \tag{4}\]  
\[Cg=128+\left(-81.085\times R^{\prime}\right)+\left(112\times G^{\prime}\right) +\left(-30.915\times B^{\prime}\right) \tag{5}\]  
\[Cb=128+\left(-37.797\times R^{\prime}\right)+\left(-74.203\times G^{\prime} \right)+\left(112\times B^{\prime}\right) \tag{6}\]  
\[C_{max}\ =max\left(R^{\prime},\ G^{\prime},\ B^{\prime}\right) \tag{7}\]  
\[C_{min}=min\left(R^{\prime},\ G^{\prime},\ B^{\prime}\right) \tag{8}\]  
\[\Delta=C_{max}-C_{min} \tag{9}\]  
\[H=\left\{ \begin{array}{ll}60^{\circ}\times\left(\frac{G^{\prime}-B^{\prime} }{\Delta}mod6\right)&,\ if\ C_{max}=R^{\prime}\\ 60^{\circ}\times\left(\frac{B^{\prime}-R^{\prime}}{\Delta}+2\right)&,\ if\ C_{max}=G^{ \prime}\\ 60^{\circ}\times\left(\frac{R^{\prime}-G^{\prime}}{\Delta}+4\right)&,\ if\ C_{max}=B^{ \prime}\end{array} \right. \tag{10}\]  
\[S=\left\{ \begin{array}{ll}0&,\ if\ C_{max}=0\\ \frac{\Delta}{C_{max}}&,\ if\ C_{max}\
eq 0\end{array} \right. \tag{11}\]  
\[V=C_{max} \tag{12}\]  
Figure 4: Comparison of the signals obtained from the Logitech C920e webcam, iPhone 14, and Galaxy Z Flip4. Using the RGB signals and the transformed YCrCgCb and HSV signals, a dataset of size N \(\times\) 10 was generated, where N represents the number of frames in the video.