Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces

Constantino Alvarez Casado, Miguel Bordallo Lopez

Research output: Contribution to journalArticleScientificpeer-review

15 Citations (Scopus)


Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurably. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.

Original languageEnglish
Pages (from-to)5530-5541
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number11
Publication statusPublished - 1 Nov 2023
MoE publication typeA1 Journal article-refereed


  • Biosignals
  • Face Analysis
  • Pulse rate estimation
  • Remote Photoplethysmography
  • rPPG
  • Signal Processing


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