# Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
## 2 Materials and Methods
### Studied Approaches with and without ROI Localization Step  
We applied five different approaches for PR extraction from video recordings--four with the ROI localization step and one without it:  
* Viola-Jones [26] frontal face detector + ROI width reduction to 60% of its original width [7] + KLT tracker [27; 37] (hereinafter VK),
* Viola-Jones [26] frontal face detector + facial landmarks detector [13] + KLT tracker [27; 37] (hereinafter VK-LMK),
* Viola-Jones [26] frontal face detector + RGB-H-CbCr skin-color segmentation method [11] + KLT tracker [27; 37] (hereinafter VK-RGBHCbCr),
* Viola-Jones [26] frontal face detector + skin detector on the maximization of mutual information [15] + KLT tracker [27; 37] (hereinafter VK-Conaire) and
* Full Video Pulse Extraction [16] (hereinafter FVP), which is by default combined with the POS rPPG algorithm.  
The basis for all approaches with an image processing front-end is a combination of Viola-Jones frontal face detector and KLT tracker due to its predominant application in rPPG studies. Viola-Jones frontal face detector was applied on the first frame of each recording. In the case more than one face was detected, the largest bounding box was selected for further processing. From this step onwards, the methodology split in separate directions:  
* In the VK approach, we reduced the width of the detected ROI to 60% of its original width [7].
* In VK-LMK we identified facial landmarks within the original (unresized) ROI containing a detected face using the Discriminative Response Map Fitting approach [13]. From the identified 66 landmarks, we used nine of them to define new ROI, which covered cheeks, nose, and mouth area [38].
* In VK-RGBHCbCr and VK-Conaire, we kept the original size of the detected ROI.  
The KLT tracker was then initialized by identifying feature points within the original bounding box containing the detected face using Good Features to Track method [37] and then propagated throughout the entire recording. For a current frame, the tracker attempted to find the points from the previous frame and then estimate a transformation matrix consisting of the affinity parameters between the old points from the previous frame and new points from the current frame. Estimation of the transformation matrix was done using MSAC algorithm [39], a type of RANSAC algorithm, which is, in general,used for robust estimation of parameters of a selected mathematical model. Once the transformation matrix was defined, it was used to transform the edges of the ROIs defined in the list above from a previous frame to a current one. In the case of VK and VK-LMK, the newly transformed ROI represented the final ROI that was used for further processing, whereas in the case of VK-RGBHCbCr and VK-Conaire, the ROI was refined using RGB-H-CbCr skin color segmentation model [11], and skin detector based on the maximization of mutual information [15] were applied, respectively. The presented steps were applied on each of the following frames. The only studied approach without the ROI localization step was FVP [16]. Its implementation followed that of its authors [16], with an exception that we did not remove non-pulsatile components from rPPG signals in order to ensure easier comparison with other studied approaches. The selection of the studied approaches with image processing front-end was based on the frequency of their use in research--[PERSON] et al. reported that the most cited detection and tracking algorithm in rPPG research are Viola-Jones face detector, facial landmarks, KLT, and skin detection [8].