# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 6 Discussion and Conclusions  
We were able to obtain successful and promising results from our appearance modelling and signal-processing based rPPG method. The results show that this unsupervised method achieves high accuracies, matching or surpassing state-of-the-art on six public datasets: PURE, VIPL-HR, MoLi-PPG, VicarPPG, UBFC-RPPG, and MMSE-HR. In fact, the accuracy of our method for heart rate analysis is in the same range or beyond several fully supervised deep learning methods, albeit without any rPPG specific training or fine-tuning. We also surpass all existing methods for heart rate variability estimation and set some of the first benchmarks for heart rate variability analysis on these datasets. Through an exhaustive 13-dataset evaluation (including release of a new public dataset: VicarPPG 2), the strengths and weaknesses of our method were highlighted. We showed that the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Face Finding \& Modelling (ms)** & **Skin Pixel Selection (ms)** & **rPPG Algorithm (ms)** & **Total (ms)** & **Frame Rate** \\ \hline
**31.89 \(\pm\) 17.2** & **0.43 \(\pm\) 0.2** & **0.56 \(\pm\)** 0.2 & **32.88 \(\pm\) 18.8 & \(\mathbf{-30.4\) fps** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The processing speed of individual components of the proposed method’s pipeline and the total frame rate (640 \(\times\) 480 pixel input) on an Intel Xeon E5 CPU. The main bottleneck is face finding and modelling, while the rest require negligible time. Figure 13: rPPG performance (MAE; error bars represent standard deviation) w.r.t camera type. HD cameras produce the best results likely due to their superior sensor. Modern webcams perform fairly well too. proposed method handles most realistic variations in illumination, movement, and facial activity well. This can be attributed to the appearance modelling and noise suppression steps in the pipeline. However, certain combinations and extreme cases of these conditions proved challenging: overtly bright or flickering lighting and large head and body movements (e.g., during exercising). Our study provided some unique insights about the rPPG analysis in terms of performance while eating and emoting facial expressions: chewing motion during eating did not result in larger errors, while HR analysis during high facial arousal proved marginally challenging. We also explicitly studied the impact of additional recording factors like video compression and camera type: H.265 and FFV1 emerged as clear winners in terms of preserving plethysmographic information in the skin pixels efficiently; higher quality rPPG signal can be obtained from HD cameras, but modern webcams also provide good results. High video compression noise was observed to be a clear limitation of our signal-processing method, especially in comparison with deep learning based method. Several deep learning methods have shown good results on such datasets, while they fail to match our method in cases with lower compression. This could be because the deep network is able to learn the spatial patterns of this compression noise and filter them out. In contrast, in lower compression cases, our prior domain knowledge assumptions perform more accurately. While this makes our method well suited for modern videos, deep learning might be better suited for processing archival videos, often subject to higher compression. Finally, we demonstrated how the ground truth PPG and ECG signals provided with most datasets can be highly noisy, leading to incorrect peak detection and resulting in substantial miscalculation of heart rate and heart rate variability measures. To tackle this, we introduced the CleanerPPG set: a collection of hand-cleaned ground truth peaks for 13 major public datasets. Using this ground truth ensures a fairer and more accurate evaluation. This set is intended to continuously grow with community support. Conceptualization, A.G. and M.B. ; methodology, A.G. and M.B. ; software, M.B. ; validation, A.G. and M.B. ; formal analysis, A.G. and M.B.