Dense-HOG-based drift-reduced 3D face tracking for infant pain monitoring

R.W.J.J. Saeijs, W.E. Tjon A Ten, P.H.N. de With

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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%.
Original languageEnglish
Publication statusPublished - 2016
Event9th International Conference on Machine Vision (ICMV 2016) - Nice, France
Duration: 18 Nov 201620 Nov 2016
Conference number: 9


Conference9th International Conference on Machine Vision (ICMV 2016)
Abbreviated titleICMV 2016
Internet address


  • face tracking
  • pain monitoring
  • cylinder head model
  • dense HOG

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