Dense-Hog-based 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

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. The algorithm uses a cylinder head model and 3D head pose recovery by alignment of dynamically extracted templates based on dense-HOG features. The algorithm includes extensions for drift reduction, using re-registration in combination with multi-pose state estimation by means of a square-root unscented Kalman filter. The paper reports experimental results on videos of moving infants in hospital who are relaxed or in pain. Results show good tracking behavior for poses up to 50 degrees from upright-frontal. In terms of eye location error relative to inter-ocular distance, the mean tracking error is below 9%