Skin Segmentation for Imaging Photoplethysmography Using a Specialized Deep Learning Approach
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Imaging photoplethysmography (iPPG) is a camera-based approach for the remote measurement of superficial tissue perfusion most commonly applied to facial video recordings. Since only tissue contains information about perfusion, skin detection is a necessary processing step. Several approaches for the detection of skin pixels in video recordings have been developed, e.g. using color thresholds. Within this work we present a deep learning based approach capable of combining color and morphology information, which makes the skin detection robust against different illumination conditions. We evaluated our new approach using two datasets with 182 individuals of different gender, age, skin tone and illumination conditions. Our approach outperformed state-of-the-art algorithms or yielded at least comparable results (mean absolute error of estimated pulse rate improved by up to 68 %). The method presented allows more accurate assessment of superficial tissue perfusion with iPPG.
Details
Original language | English |
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Title of host publication | 48th Conference Computing in Cardiology (CinC) |
Publisher | Wiley-IEEE Press |
Pages | 1-4 |
Number of pages | 4 |
Volume | 48 |
ISBN (electronic) | 9781665479165 |
ISBN (print) | 978-1-6654-6721-6 |
Publication status | Published - 15 Sept 2021 |
Peer-reviewed | Yes |
Conference
Title | 2021 Computing in Cardiology (CinC) |
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Abbreviated title | CinC 2021 |
Conference number | 48 |
Duration | 12 - 15 September 2021 |
Website | |
Degree of recognition | International event |
Location | Hotel Passage & online |
City | Brno |
Country | Czech Republic |
External IDs
Scopus | 85124729976 |
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ORCID | /0000-0001-6754-5257/work/142232820 |
ORCID | /0000-0003-4012-0608/work/142235698 |
Keywords
Research priority areas of TU Dresden
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- Deep learning, Image color analysis, Statistical analysis, Neural networks, Lighting, Imaging, Photoplethysmography