  Automatic continuous time, continuous value assessment of a patient's pain
from face video is highly sought after by the medical profession. Despite the
recent advances in deep learning that attain impressive results in many
domains, pain estimation risks not being able to benefit from this due to the
difficulty in obtaining data sets of considerable size. In this work we propose
a combination of hand-crafted and deep-learned features that makes the most of
deep learning techniques in small sample settings. Encoding shape, appearance,
and dynamics, our method significantly outperforms the current state of the
art, attaining a RMSE error of less than 1 point on a 16-level pain scale,
whilst simultaneously scoring a 67.3% Pearson correlation coefficient between
our predicted pain level time series and the ground truth.
