# Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
## 3 Results
Figure 3 shows the SNR results of the studied approaches tested on PBDTrPPG. Figure 3: Performance of the studied approaches with and without image processing front-end in combination with two rPPG algorithms (POS and 2 SR) in terms of SNR on the example of PBDTrPPG covering **(a)** different lighting conditions (LC1: \(0.052\times 10^{2}\) lux, LC2: \(0.363\times 10^{2}\) lux, LC3: \(1.870\times 10^{2}\) lux, LC4: \(7.200\times 10^{2}\) lux, LC5: \(27.200\times 10^{2}\) lux) and **(b)** skin tone challenges. VK: Viola-Jones combined with ROI width reduction and Kanade-Lucas-Tomasi tracker; VK-Conaire: Viola-Jones combined with skin detector based on the maximization of mutual information and Kanade–Lucas–Tomasi tracker; VK-LMK: Viola-Jones combined with landmarks detection and Kanade–Lucas–Tomasi tracker; VK-RGBHCbCr: Viola-Jones combined with RGB–H–CbCr skin-color segmentation and Kanade–Lucas–Tomasi tracker; FVP: Full Video Pulse Extraction rPPG algorithm. Figure 3a shows that mean SNR values increase with an increasing lighting intensity in all studied approaches. Performances of various approaches are comparable except for FVP, which shows weaker performance at all lighting intensities, as well as VK-Conaire-2 SR and VK-Conaire-POS at the largest lighting intensity. For the bright and dark skin tone recordings, the results of one-way ANOVA show no statistically significant differences in mean SNR values between all pairs of studied approaches (\(p=0.8032\) and \(p=0.6831\), respectively). For the intermediate skin tone recordings, there are statistically significant differences in mean SNR values between FVP and (1) VK-POS (\(p=0.0003\)), (2) VK-2 SR (\(p=0.0003\)), (3) VK-LMK-POS (\(p<0.0001\)), (4) VK-LMK-2 SR (\(p<0.0001\)), (5) VK-RGBHCbCR-POS (\(p=0.0002\)), (6) VK-RGBHCbCR-2 SR (\(p=0.0002\)), (7) VK-Conaire-POS (\(p=0.0008\)) and (8) VK-Conaire-2 SR (\(p=0.0012\)). In all the studied recordings from LGI-PPGI-FVD, FVP exhibits the widest interquartile range. Figure 4 shows the performance of the studied methods for extracting rPPG signals from video recordings from LGI-PPGI-FVD in terms of SNR. There are no significant differences between the SNR values among the studied approaches in case of resting scenario, whereas in the case of gym recordings, FVP performed worse than the other approaches. However, it should be noted that in the case of the approaches with the image-processing front-end, four of the video recordings were not processed successfully. The SNR values of the rPPG signals extracted from these recordings are the ones that lower the mean SNR value for FVP. Table 3 shows the results of the RMSE analysis. The results follow those of SNR, but the differences between the studied approaches seem to be more prominent. On average, the best performance is achieved by a combination of the VK approach and the SB rPPG algorithm. As expected, the RMSE is the largest in the case of low lighting conditions (LC1) and dark skin tone and the lowest in the case of resting conditions regardless of the applied approach. Similarly as in the case of SNR values, the worst performance was achieved by FVP. Figure 4: Performance of the studied approaches with and without image processing front-end in combination with two rPPG algorithms (POS and 2 SR) in terms of SNR on the example of LGI-PPGI-FVD. VK: Viola-Jones combined with ROI width reduction and Kanade–Lucas–Tomasi tracker; VK-Conaire: Viola-Jones combined with skin detector based on the maximization of mutual information and Kanade–Lucas–Tomasi tracker; VK-LMK: Viola-Jones combined with landmarks detection and Kanade–Lucas–Tomasi tracker; VK-RGBHCbCr: Viola-Jones combined with RGB–H–CbCr skin-color segmentation and Kanade–Lucas–Tomasi tracker; FVP: Full Video Pulse Extraction rPPG algorithm.