  Managing post-surgical pain is critical for successful surgical outcomes. One
of the challenges of pain management is accurately assessing the pain level of
patients. Self-reported numeric pain ratings are limited because they are
subjective, can be affected by mood, and can influence the patient's perception
of pain when making comparisons. In this paper, we introduce an approach that
analyzes 2D and 3D facial keypoints of post-surgical patients to estimate their
pain intensity level. Our approach leverages the previously unexplored
capabilities of a smartphone to capture a dense 3D representation of a person's
face as input for pain intensity level estimation. Our contributions are adata
collection study with post-surgical patients to collect ground-truth labeled
sequences of 2D and 3D facial keypoints for developing a pain estimation
algorithm, a pain estimation model that uses multiple instance learning to
overcome inherent limitations in facial keypoint sequences, and the preliminary
results of the pain estimation model using 2D and 3D features with comparisons
of alternate approaches.
