Remote Photoplethysmography Based on Implicit Living Skin Tissue Segmentation

Abstract : Region of interest selection is an essential part for remote photoplethysmography (rPPG) algorithms. Most of the time, face detection provided by a supervised learning of physical appearance features coupled with skin detection is used for region of interest selection. However, both methods have several limitations and we propose to implicitly select living skin tissue via their particular pulsatility feature. The input video stream is decomposed into several temporal superpixels from which pulse signals are extracted. Pulsatility measure for each temporal superpixel is then used to merge pulse traces and estimate the photoplethysmogram signal. This allows to select skin tissue and furthermore to favor areas where the pulse trace is more predominant. Experimental results showed that our method perform better than state of the art algorithms without any critical face or skin detection.
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Communication dans un congrès
IEEE. 23rd International Conference on Pattern Recognition (ICPR 2016), Dec 2016, Cancun, Mexico. 2016, <http://www.icpr2016.org/site/>
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01356059
Contributeur : Yannick Benezeth <>
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Dernière modification le : vendredi 26 août 2016 - 01:01:25
Document(s) archivé(s) le : samedi 26 novembre 2016 - 13:17:53

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Serge Bobbia, Yannick Benezeth, Julien Dubois. Remote Photoplethysmography Based on Implicit Living Skin Tissue Segmentation. IEEE. 23rd International Conference on Pattern Recognition (ICPR 2016), Dec 2016, Cancun, Mexico. 2016, <http://www.icpr2016.org/site/>. <hal-01356059>

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