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Xi, Lin 
ORCID: https://orcid.org/0000-0001-6075-5614, Chen, Weihai, Zhao, Changchen, Wu, Xingming and Wang, Jianhua
(2021)
Image Enhancement for Remote Photoplethysmography in a Low-Light Environment.
In:
Proceedings of the 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020).
The Institute of Electrical and Electronics Engineers (IEEE), ARG, pp. 761-764.
ISBN 9781728130798
Abstract
With the improvement of sensor technology and significant algorithmic advances, the accuracy of remote heart rate monitoring technology has been significantly improved. Despite of the significant algorithmic advances, the performance of rPPG algorithm can degrade in the long-term, high-intensity continuous work occurred in evenings or insufficient light environments. One of the main challenges is that the lost facial details and low contrast cause the failure of detection and tracking. Also, insufficient lighting in video capturing hurts the quality of physiological signal. In this paper, we collect a largescale dataset that was designed for remote heart rate estimation recorded with various illumination variations to evaluate the performance of the rPPG algorithm (Green, ICA, and POS). We also propose a low-light enhancement solution (technical solution) for remote heart rate estimation under the low-light condition. Using collected dataset, we found 1) face detection algorithm cannot detect faces in video captured in low light conditions; 2) A decrease in the amplitude of the pulsatile signal will lead to the noise signal to be in the dominant position; and 3) the chrominance-based method suffers from the limitation in the assumption about skin-tone will not hold, and Green and ICA method receive less influence than POS in dark illuminance environment. The proposed solution for rPPG process is effective to detect and improve the signal-to-noise ratio and precision of the pulsatile signal.
| Item Type: | Book Section |
|---|---|
| Additional Information: | Funding Information: This research was sponsored by National Natural Science Foundation of China under Grant No. 61620106012, 61903336, 61773042, 51675018. Publisher Copyright: © 2020 IEEE. |
| Uncontrolled Keywords: | computer vision and pattern recognition,artificial intelligence ,/dk/atira/pure/subjectarea/asjc/1700/1707 |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 05 Nov 2024 12:30 |
| Last Modified: | 12 Nov 2024 13:31 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/97512 |
| DOI: | 10.1109/FG47880.2020.00076 |
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