  We present a lightweight neural model for remote heart rate estimation
focused on the efficient spatio-temporal learning of facial
photoplethysmography (PPG) based on i) modelling of PPG dynamics by
combinations of multiple convolutional derivatives, and ii) increased
flexibility of the model to learn possible offsets between the facial video PPG
and the ground truth. PPG dynamics are modelled by a Temporal Derivative Module
(TDM) constructed by the incremental aggregation of multiple convolutional
derivatives, emulating a Taylor series expansion up to the desired order.
Robustness to ground truth offsets is handled by the introduction of TALOS
(Temporal Adaptive LOcation Shift), a new temporal loss to train learning-based
models. We verify the effectiveness of our model by reporting accuracy and
efficiency metrics on the public PURE and UBFC-rPPG datasets. Compared to
existing models, our approach shows competitive heart rate estimation accuracy
with a much lower number of parameters and lower computational cost.
