# Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
###### Abstract  
Traditionally, the first step in physiological measurements based on remote photoplethysmography (rPPG) is localizing the region of interest (ROI) that contains a desired pulsatile information. Recently, approaches that do not require this step have been proposed. The purpose of this study was to evaluate the performance of selected approaches with and without ROI localization step in rPPG signal extraction. The [PERSON] face detector and Kanade-Lucas-Tomasi tracker (VK) in combination with (a) a region-of-interest (ROI) cropping, (b) facial landmarks, (c) skin-color segmentation, and (d) skin detection based on maximization of mutual information and an approach without ROI localization step (Full Video Pulse (FVP)) were studied. Final rPPG signals were extracted using selected model-based and data-driven rPPG algorithms. The performance of the approaches was tested on three publicly available data sets offering compressed and uncompressed video recordings covering various scenarios. The success rates of pulse waveform signal extraction range from 88.37% (VK with skin-color segmentation) to 100% (FVP). In challenging scenarios (skin tone, lighting conditions, exercise), there were no statistically significant differences between the studied approaches in terms of SNR. The best overall performance in terms of RMSE was achieved by a combination of VK with ROI cropping and the model-based rPPG algorithm. Results indicate that the selection of the ROI localization approach does not significantly affect rPPG measurements if combined with a robust algorithm for rPPG signal extraction. remote photoplethysmography; vital signs monitoring; remote sensing +
Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P.) Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P.) Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 100 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P.) Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P.) Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 100 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P.) Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P.) Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.)  
Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P.) Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 100 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P.) Footnote †: journal: Medical Engineering, University of Ljubljana, Askereva cesta 6, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (Z.P. ); [EMAIL_ADDRESS] (P.P. )and the appearance of face in the recordings. In the second approach, video recordings are first segmented into several regions, from which rPPG pulse waveform signals are extracted. Next, based on the characteristics of rPPG signals, pulse and noise are differentiated from the extracted signals, and finally, the regions containing pulse information are labeled as skin regions [5]. Drawbacks of this approach are its high computational complexity, the presence of the causality dilemma caused by the interdependence of the skin detection and pulse waveform signal extraction, and the requirement for spatio-temporally coherent local segmentation [16].