Dense-HOG-Based Drift-Reduced 3D Face Tracking for Infant Pain Monitoring

This paper presents a new algorithm for 3D face tracking intended
for clinical infant pain monitoring under challenging conditions. The
algorithm uses a cylinder head model and head pose recovery by
alignment of dynamically extracted templates based on dense-HOG
features. The algorithm is motivated from the application context
and compared with a variant based on intensities. The paper reports
experimental results on videos of moving infants in hospital who are
relaxed or in pain. Results show good short-term tracking behavior
for poses up to 50 degrees from upright-frontal, with significantly
higher accuracy resulting from the use of dense-HOG features.