  Heart rate measuring based on remote photoplethysmography (rPPG) plays an
important role in health caring, which estimates heart rate from facial video
in a non-contact, less-constrained way. End-to-end neural network is a main
branch of rPPG-based heart rate estimation methods, whose trait is recovering
rPPG signal containing sufficient heart rate message from original facial video
directly. However, there exists some easily neglected problems on relevant
datasets which thwarting the efficient training of end-to-end methods, such as
uncertain temporal delay and indefinite envelope shape of label waves. Although
many novel and powerful networks are proposed, hitherto there are no systematic
research digging into these problems. In this paper, from perspective of common
intrinsic rhythm periodical self-similarity results from cardiac activities, we
propose a comprehensive methodology, Boost Your Heartbeat Estimation (BYHE),
including new label representations, corresponding network adjustments and loss
functions. BYHE can be easily grafted on current end-to-end network and boost
its training efficiency. By applying our methodology, we can save tremendous
time without conducting laborious handworks, such as label wave alignment which
is necessary for previous end-to-end methods, and meanwhile enhance the
utilization on datasets. According to our experiments, BYHE can leverage
classical end-to-end network to reach competitive performance against those
state-of-the-art methods on mostly used datasets. Such improvement indicates
selecting perspicuous and efficient label representation is also a promising
direction towards better remote physiological signal measurement.
