  We propose an automatic method for pain intensity measurement from video. For
each video, pain intensity was measured using the dynamics of facial movement
using 66 facial points. Gram matrices formulation was used for facial points
trajectory representations on the Riemannian manifold of symmetric positive
semi-definite matrices of fixed rank. Curve fitting and temporal alignment were
then used to smooth the extracted trajectories. A Support Vector Regression
model was then trained to encode the extracted trajectories into ten pain
intensity levels consistent with the Visual Analogue Scale for pain intensity
measurement. The proposed approach was evaluated using the UNBC McMaster
Shoulder Pain Archive and was compared to the state-of-the-art on the same
data. Using both 5-fold cross-validation and leave-one-subject-out
cross-validation, our results are competitive with respect to state-of-the-art
methods.
