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

Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation