# Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
## 4 Discussion  
FVP is the only approach that allowed continuous PR measurement in all studied video recordings. It would, however, fail in the case of video recordings of multiple subjects, and its performance would deteriorate in the case of significant background changes. In other approaches, face detector algorithm or KLT tracker failed in at least one of the video recordings. In general, approaches relying on [PERSON] frontal face detector would provide false positive results in the case of mannequins and fail completely if body parts other than face were covered on video recordings. Mean SNR and RMSE values indicate that, in most of the recording scenarios, the performance of the studied approaches is comparable. This means that (1) the obtained ROIs were large enough to cancel out the camera quantization noise and (2) the effect of non-skin pixels did not significantly affect the performance of the studied approaches. When comparing the results of the approaches combined with POS with those combined with 2 SR, the POS-based solutions seem to outperform the 2 SR-ones, especially in the case of more challenging scenarios. This confirms the innate sensitivity of 2 SR to noisy skin mask. In addition, there are some observable differences between the studied approaches in some recording scenarios. In the talking-scenario recordings from PURE (Figure 2), the performance of all the approaches decreases in comparison to resting scenario due to noisier ROIs and varying intensities of specular and diffuse reflection components due to the motion of the facial pixels during talking. The worst performance is achieved by VK-LMK-2 SR due to the largest ratio of non-skin to skin pixels (ROIs in VK-LMK were the smallest out of all ROIs), which arises when the mouth is opened during talking. A similar result is achieved in the head rotation scenario, in which we can also observe that 2 SR performs worse than POS. In the case of PBDTTPPG (see Figure 3), which contains lossy compressed video recordings, average SNRs are the lowest out of all studied data sets. The achieved SNR values are comparable, and the selection of the approach with or without an image processing front-end together with an rPPG algorithm does not affect SNR value significantly. This is most likely due to partial loss of pulsatile information or introduction of additional artifacts into the recordings, as sugggested by [PERSON] et al. [31]. Results show that SNR values increase as light intensity increases (Figure 3a) and decreases as skin tone decreases (Figure 3b), which is expected based on the knowledge underlying the formation of rPPG signal. It should be noted that results for different skin tones are not directly comparable, because of the variable distance between subjects and a camera. In addition, intermediate and dark skin tone would, based on our perception, lie close to each other on the Fitzpatrick scale, which could explain similar SNR values achieved. Interestingly, the best performance in case of a dark skin tone was achieved by FVP, which might be due to the fact that it relies not only on a mean but also on a variance for spatial pixel combination [31]. Lastly, the results of SNR values for LGI-PPGI-FVD (Figure 4) indicate similar performance of studied approaches in both resting and gym scenarios (except for FVP at gym recordings). In the case of the gym scenario, average SNR values are greatly reduced and only up to two video recordings from the set were successfully processed using the studied approaches with image processing front-end, whereas FVP allowed processing of all videos. It is to be noted that individual SNR values that lie below the mean SNR value for FVP correspond to the videos in which other approaches failed. If we removed these results, the SNR values of FVP would be comparable to those of the other approaches. Provided that the relative ROI sizes for recordings from LGI-PPGI-FVD were the largest, unevenly distributed pulsations [40] might also affect the results.