Skip to main content
SpringerLink
Log in

Towards Estimating Heart Rates from Video Under Low Light

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

Included in the following conference series:

  • 1868 Accesses

Abstract

The ability to read the physiological state of a person using conventional cameras opens the doors to many potential applications such as medical monitoring, human emotion recognition, and even human robot interaction. The estimation of heart rates from video is particularly useful and well suited to reading from conventional cameras as evidenced by a body of recent literature. However, existing work has only been demonstrated to work under relatively good lighting, which limits the range of applications. In this paper, we propose a new approach towards estimating heart rate from video that is robust to low light conditions in addition to motion and changing illuminants. The approach is simple, fast, and we show that it captures the HR effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Some of the patch pairs were ruled out using heuristics.

References

  1. Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16, 21434–21445 (2008)

    Article  Google Scholar 

  2. McDuff, D.J., Estepp, J.R., Piasecki, A.M., Blackford, E.B.: A survey of remote optical photoplethysmographic imaging methods. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6398–6404 (2015)

    Google Scholar 

  3. Cennini, G., Arguel, J., Akşit, K., van Leest, A.: Heart rate monitoring via remote photoplethysmography with motion artifacts reduction. Opt. Express 18, 4867–4875 (2010)

    Article  Google Scholar 

  4. Poh, M.Z., McDuff, D., Picard, R.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58, 7–11 (2011)

    Article  Google Scholar 

  5. Kwon, S., Kim, H., Park, K.S.: Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. In: IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2174–2177 (2012)

    Google Scholar 

  6. Li, X., Chen, J., Zhao, G., Pietikainen, M.: Remote heart rate measurement from face videos under realistic situations. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 4264–4271 (2014)

    Google Scholar 

  7. Kumar, M., Veeraraghavan, A., Sabharwal, A.: DistancePPG: robust non-contact vital signs monitoring using a camera. Biomed. Opt. Express 6, 1565–1588 (2015)

    Article  Google Scholar 

  8. Lam, A., Kuno, Y.: Robust heart rate measurement from video using select random patches. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3640–3648 (2015)

    Google Scholar 

  9. Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J.F., Sebe, N.: Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  10. Balakrishnan, G., Durand, F., Guttag, J.: Detecting pulse from head motions in video. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 3430–3437 (2013)

    Google Scholar 

  11. Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.T.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. (Proceedings SIGGRApPH 2012) 31 (2012)

    Google Scholar 

  12. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)

    Article  Google Scholar 

  13. Moreno, J., Ramos-Castro, J., Movellan, J., Parrado, E., Rodas, G., Capdevila, L.: Facial video-based photoplethysmography to detect HRV at rest. Int. J. Sports Med. 36, 474–480 (2015)

    Article  Google Scholar 

  14. Chai, D., Ngan, K.N.: Face segmentation using skin-color map in videophone applications. IEEE Trans. Circuits Syst. Video Technol. 9, 551–564 (1999)

    Article  Google Scholar 

  15. Tarvainen, M., Ranta-aho, P., Karjalainen, P.: An advanced detrending method with application to HRV analysis. IEEE Trans. Biomed. Eng. 49, 172–175 (2002)

    Article  Google Scholar 

  16. Welch, P.D.: The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15, 70–73 (1967)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Saitama University, Saitama, Japan

    Antony Lam & Yoshinori Kuno

Authors
  1. Antony Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshinori Kuno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antony Lam .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Lam, A., Kuno, Y. (2016). Towards Estimating Heart Rates from Video Under Low Light. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50832-0_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation