Abstract
The Waist-to-Hip Ratio (WHR) is an important indicator for health risk prediction, body fat distribution, body shape analysis, and physical fitness analysis. The conventional approach for obtaining the WHR entails manual measurement, which necessitates experienced anthropometrists to measure the waist and hip circumferences of a subject wearing tight clothing in a predetermined posture, and subsequently calculate the ratio based on the acquired measurements. WHR errors may be accumulated due to the anthropometrist’s subjectivity, as well as the person’s pose and attire during the measurement process. Non-contact anthropometric measurements using 3D scanning technology have shown promise in providing higher accuracy and faster measurement compared to traditional methods. However, they require complete undressed body scans as input, which is not always available. In this paper, we proposed, to the best of our knowledge, the first deep learning-based algorithm, dubbed W2H-Net, to predict the WHR directly from single partial dressed body scans in arbitrary postures. W2H-Net introduces a novel framework called FocusNet to improve learning accuracy by selectively focusing on parts that require attention. W2H-Net provides a flexible, cost-effective, and privacy-preserving way to obtain accurate WHR measurements, which are crucial for predicting health risks associated with central obesity. Extensive experimental results can demonstrate the superiority of the proposed method.
Original language | English |
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Title of host publication | 2023 IEEE International Joint Conference on Biometrics, IJCB 2023 |
Publisher | IEEE |
Number of pages | 10 |
ISBN (Electronic) | 9798350337266 |
ISBN (Print) | 9798350337273 |
DOIs | |
Publication status | E-pub ahead of print - 1 Mar 2024 |
Event | IEEE International Joint Conference on Biometrics - Ljubljana, Slovenia Duration: 25 Sept 2023 → 28 Sept 2023 https://ijcb2023.ieee-biometrics.org/ |
Publication series
Name | 2023 IEEE International Joint Conference on Biometrics, IJCB 2023 |
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ISSN (Print) | 2474-9680 |
ISSN (Electronic) | 2474-9699 |
Conference
Conference | IEEE International Joint Conference on Biometrics |
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Abbreviated title | IJCB 2023 |
Country/Territory | Slovenia |
City | Ljubljana |
Period | 25/09/23 → 28/09/23 |
Internet address |
Bibliographical note
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Funding
This work was supported in part by Innoviris under project AI43D and in part by Fonds Wetenschappelijk Onderzoek (FWO) under Project G094122N. This work was supported in part by Innoviris under project AI43D and in part by Fonds Wetenschappelijk On-derzoek (FWO) under Project G094122N.
Funders | Funder number |
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Innoviris | AI43D |
Fonds Wetenschappelijk Onderzoek | G094122N |
Keywords
- Privacy
- Obesity
- Three-dimensional displays
- Shape
- Prediction algoerithms
- Hip
- Synthetic data