Volume 15 Issue 2
Mar.  2022
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JI Xiao-qiang, LIU Zhen-yao, LI Bing-lin, RAO Zhi, LI Gui-wen, SU Li-wei. Non-contact perception of physiological parameters from videos of faces[J]. Chinese Optics, 2022, 15(2): 276-285. doi: 10.37188/CO.2021-0157
Citation: JI Xiao-qiang, LIU Zhen-yao, LI Bing-lin, RAO Zhi, LI Gui-wen, SU Li-wei. Non-contact perception of physiological parameters from videos of faces[J]. Chinese Optics, 2022, 15(2): 276-285. doi: 10.37188/CO.2021-0157

Non-contact perception of physiological parameters from videos of faces

doi: 10.37188/CO.2021-0157
Funds:  Supported by Department of Science and Technology of Natural Science Foundation of Jilin Province under grant number (No. 20210204131YY)
More Information
  • Corresponding author: zuoanmulan@163.com
  • Received Date: 12 Aug 2021
  • Rev Recd Date: 14 Sep 2021
  • Available Online: 19 Oct 2021
  • Publish Date: 21 Mar 2022
  • Non-contact detection of various physiological parameters has attract great attention. In this paper, a method of estimating physiological parameters based on imaging photoplethysmography from videos of people’s faces recorded by mobile phone is proposed. First, a "wavelet transform-principal component analysis-blind source separation" algorithm is proposed to extract the video’s RGB three-channel pulse wave signal with a high signal-to-noise ratio. Then, the green channel signal is processed separately in the frequency and the time domains to estimate heart and respiratory rates. The pulse wave signals of the red and blue channels are processed, and combined with the oxygen saturation detected by an oximeter to perform data fitting, the best linear equation for estimating the oxygen saturation value from the facial video is found. Finally, the error of the estimation results of various physiological parameters under natural light is compared, and the estimation results of each parameter under three lighting environments are analyzed. The results show that under the three lighting environments, the average error of heart rate detection is 0.5512 time/min, the average error of respiration rate is −0.6321 time/min , and the average error of oxygen saturation is −0.2743%. In summary, the non-contact physiological parameter estimation method proposed in this paper is highly accurate, universally applicable and stable. Its estimation results are highly consistent with the measurement result of standard instruments, which meets the needs of daily physiological parameter measurement.

     

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