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

doi:10.1117/12.2268522

17 March 2017 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

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103411U (2017) https://doi.org/10.1117/12.2268522
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

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%.

Citation Download Citation

Ronald W.J.J. Saeijs, Walther E. Tjon A Ten, and Peter H. N. de With "Dense-HOG-based drift-reduced 3D face tracking for infant pain monitoring", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411U (17 March 2017); https://doi.org/10.1117/12.2268522

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