# Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
## 3 Results  
Studied approaches failed several times due to the following reasons: (1) no face was detected on the first frame of the recording, (2) KLT tracker lost all the feature points, (3) no skin pixels were detected using the RGB-H-CbCr skin-color detector, and (4) no skin pixels were detected using skin detector based on maximization of mutual information. Table 1 lists the recordings in which one of the presented issues occurred; it can be seen that [PERSON] frontal face detector failed in some gym recordings, KLT tracker failed (i.e., lost all tracked points) in one gym recording (in this particular recording, the subject was moving his head very fast and at some point his face was occluded), skin-color-based segmentation failed in at least one of the videos from each studied data set, and the skin detector based on maximization of mutual information failed in one gym recording only. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Issue/Data Set** & **PURE** & **PBDT\(\text{rPPG}\)** & **LGI-PPGI-FVD** \\ \hline no detected face on the first frame & / & / & _alex_gym_, _angelo_gym_, _daerid_gym_ \\ KLT tracker failure & / & / & _felix_gym_ \\ no skin pixels detected (using & 03-01, 03-04, 04-02, 05-04 & P3 LC1 & _harun_gym_ \\ VK-RGBHCbCr) & / & / & _harun_gym_ \\ no skin pixels detected (using VK-Conaire) & / & / & _harun_gym_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The identified issues during ROI localization step using the studied approaches and the IDs of the recordings in which the issues occurred. VK-Conaire: Viola-Jones combined with skin detector based on the maximization ofmutual information and Kanade–Lucas–Tomasi tracker; VK-RGBHCbCr: Viola-Jones combined with RGB-H–CbCr skin-color segmentation and Kanade–Lucas–Tomasi tracker; KLT: Kanade–Lucas–Tomasi. The reasoning behind unsuccessful skin detection is due to the limited training set applied for determining the threshold values in both skin detection algorithms, whereas in the case of Viola-Jones frontal face detector failure, the appearance of faces on the first frames was the reason for unsuccessful detection. For each occurrence of the presented issues, we discarded the obtained results and marked the processing of the particular video recording as unsuccessful. Based on the number of unsuccessfully processed recordings, we defined the success rates of the studied approaches: 88.37% of VK-RGBHCbCr, 94.19% of VK-Conaire, 95.35% of VK and VK-LMK, and 100% of FVP. Figure 1 shows the examples of successfully localized ROIs for a selected recording from each of the studied data sets using four different approaches with an image-processing front-end. The sizes of ROIs are shown in Table 2. Mean absolute ROI sizes are, in general, the largest for videos from LGI-PPGI-FVD and the smallest for videos from PURE (differences in sizes are more than two-fold); mean relative ROI sizes range from 4.9 to 13.2% of the total frame size for LGI-PPGI-FVD recordings, from 3.0 to 6.5% for PURE and from 0.6 to 1.4% for PBDT-rPPG. The differences arise from different distances between the subjects and the camera, as well as different video frame sizes between the three studied data sets. Note that FVP does not rely on a common ROI localization step; therefore it is not included in Table 2.  
Figure 1: Results of the region of interest (ROI) localization on the selected recordings from studied data sets: recording P1 LC3 from PBDTrPPG using (**a**) VK, (**b**) VK-LMK, (**c**) VK-Conaire, (**d**) VK-RGBHCbCr; recording _angelo_resting_ from LGI–PPGI–FVD using (**e**) VK, (**f**) VK-LMK, (**g**) VK-Conaire, (**h**) VK-RGBHCbCr; recording 01-01 from PURE (**i**) VK, (**j**) VK-LMK, (**k**) VK-Conaire, (**l**) VK-RGBHCbCr.