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

Ronald W.J.J. Saeijs, Walther E. Tjon A Ten, Peter 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
Title of host publicationNinth International Conference on Machine Vision, ICMV 2016
EditorsA. Verikas, P. Radeva, D.P. Nikolaev, W. Zhang, J. Zhou
Number of pages11
ISBN (Electronic)9781510611313
Publication statusPublished - 1 Jan 2017
Event9th International Conference on Machine Vision (ICMV 2016) - Nice, France
Duration: 18 Nov 201620 Nov 2016
Conference number: 9

Publication series

NameProceedings of SPIE


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


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


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