# Measuring Facial Illuminance with Smartphones and Mobile Devices
## 4 Discussion  
The goal of this project was to assess the feasibility of using the already available smart phone cameras to monitor the general lighting levels experienced by children who might be at risk of developing myopia. Although many smart phones include a light sensor, when the device is being used, the light sensor faces toward the user and thus captures the light traveling in the opposite direction to the light that creates the retinal image. However, the forward-facing camera captures an image of the face, which is itself illuminated by the same general light sources that create the retinal image (those stimuli within the visual field). Therefore, smart phone cameras offer a convenient way to assess visually relevant environmental light exposure of the device user. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & \begin{tabular}{c} **Wide Angle** \\ **Homogeneous Source** \\ \end{tabular} & \
\begin{tabular}{c} **Small Angular** \\ **Source** \\ \end{tabular} &
\begin{tabular}{c} **Office Environment** \\ \end{tabular} \\ \hline Galaxy Tab S2 & y = 0.7033x & y = 1.7879x & y = 0.7887x \\  & R\({}^{2}\) = 0.9999 & R\({}^{2}\) = 0.9997 & R\({}^{2}\) = 0.9893 \\ Samsung S6 & y = 0.5247x & y = 1.4747x & y = 0.7543x \\  & R\({}^{2}\) = 1 & R\({}^{2}\) = 0.9999 & R\({}^{2}\) = 0.9983 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Slope values and regression coefficients with each device in each of the conditions a table. Figure 5: Facial illuminance calculated after the methodology proposed with the Samsung S6 Edge Plus smartphone camera when using the phone a regular way, as in Figure 2-right (\(Y\)-axis) is plotted against illuminance measurements made with the phone’s on the face with the light meter facing the position where the phone is regularly used, as in Figure 2-left (\(X\)-axis). \begin{table}
\begin{tabular}{c c} \hline \hline
**Subject** & **Slope and Regression Coefficient** \\ \hline S1 & y = 0.9439x, R\({}^{2}\) = 0.9870 \\ S2 & y = 1.1478x, R\({}^{2}\) = 0.9458 \\ S3 & y = 1.0554x, R\({}^{2}\) = 0.9488 \\ S4 & y = 0.9073x, R\({}^{2}\) = 0.9506 \\ M1 & y = 1.0218x, R\({}^{2}\) = 0.9952 \\ M2 & y = 1.0482x, R\({}^{2}\) = 0.9714 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Slope values and regression coefficients corresponding to the regression fit lines of Figure 5.  
Our approach to assess the feasibility of using these cameras employed a calibrated illuminance meter to evaluate the smart phone built in light sensor (Figure 4), which revealed that in typical room situation, the inbuilt light sensor provided illuminance measures closely matching those of the calibrated illuminance meter. This result is encouraging in that it enables the smart phone light meter to be employed as a way to calibrate the camera for light measurements (see method of calibration, Figure 2), and the results (Figure 5) indicate accurate estimates of face illuminance can be obtained using the camera, and thus can be obtained in real-time over large durations once this initial calibration has been completed. Errors in face illuminance measures made with the camera are relatively small (mean error = 27%) and skin reflectance also has a relatively minor impact. The small magnitude of these errors is insignificant in the broad context of assessing the overall light levels experienced by children, which will vary by more than 1000 times when moving from outside on a bright sunny day to a dimly lit room at home. Calibration of the built-in light sensor showed an underestimation of about 50% when a homogeneous illumination source was used that extended over a solid angle of 2\(\pi\) sr (Figure 4a) and an overestimation of about 80% when the illuminant was a small angle source in an otherwise dark field (Figure 4b). These inconsistencies between the device light sensor and the lux meter measurements reflect a combination of gain/sensitivity of the sensors as well as the angular selectivity of the sensors, which depend on their optical design.