Unsupervised skin tissue segmentation for remote photoplethysmography

•A novel method for selecting the ROI for remote photoplethysmography algorithms is proposed.•Skin tissues are implicitly selected via their distinct pulsatility feature.•A new publicly available dataset for rPPG algorithms evaluation is introduced. Segmentation is a critical step for many algorithm...

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Vydáno v:Pattern recognition letters Ročník 124; číslo supl; s. 82 - 90
Hlavní autoři: Bobbia, Serge, Macwan, Richard, Benezeth, Yannick, Mansouri, Alamin, Dubois, Julien
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.06.2019
Elsevier Science Ltd
Elsevier
Témata:
Algorithms
Computer Science
Computer Vision and Pattern Recognition
Face recognition
Image processing
Living skin tissue segmentation
Remote photoplethysmography
Segmentation
Signal and Image Processing
Signal quality
Skin
Unsupervised
92C55
Remote photoplethysmography
68U10
Unsupervised
Image processing
Living skin tissue segmentation
ISSN:0167-8655, 1872-7344
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Shrnutí:•A novel method for selecting the ROI for remote photoplethysmography algorithms is proposed.•Skin tissues are implicitly selected via their distinct pulsatility feature.•A new publicly available dataset for rPPG algorithms evaluation is introduced. Segmentation is a critical step for many algorithms, especially for remote photoplethysmography (rPPG) applications as only the skin surface provides information. Moreover, it has been shown that the rPPG signal is not distributed homogeneously across the skin. Most of the time, algorithms get input information from face detection provided by a supervised learning of physical appearance and skin pixel selection. However, both methods show several limitations. In this paper, we propose a simple approach to implicitly select skin tissues based on their distinct pulsatility feature. The input video frames are decomposed into several temporal superpixels from which the pulse signals are extracted. A pulsatility measure from each temporal superpixel is then used to merge the pulse traces and estimate the photoplethysmogram signal. Since the most pulsatile signals provide high quality information, areas where the information is predominant are favored. We evaluated our contribution using a new publicly available dataset dedicated to rPPG algorithms comparison. The results of our experiments show that our method outperforms state of the art algorithms, without any critical face or skin detection.
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ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2017.10.017