  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 configurable. 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.
