Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices

We propose an automatic method to estimate self-reported pain intensity based on facial landmarks extracted from videos. For each video sequence, we decompose the face into four different regions and pain intensity is measured by modeling the dynamics of facial movement using the landmarks of these regions. A formulation based on Gram matrices is used to represent the trajectory of facial landmarks on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. A curve fitting algorithm is then used to smooth the trajectories and a temporal alignment is performed to compute the similarity between the trajectories on the manifold. A Support Vector Regression classifier is then trained to encode the extracted trajectories into pain intensity levels consistent with the self-reported pain intensity measurement. Finally, a late fusion of the estimation for each region is performed to obtain the final predicted pain intensity level. The proposed approach is evaluated on two publicly available databases, the UNBCMcMaster Shoulder Pain Archive and the Biovid Heat Pain database. We compared our method to the state-of-the-art on both databases using different testing protocols, showing the competitiveness of the proposed approach.