Abstract
The ability to measure heart rate (HR) from face videos is useful in applications such as neonatal monitoring, telemedicine and affective computing. In the realistic environments, subjects often have spontaneous head movements and facial expressions which severely degrade the performances of the current methods. We propose a novel patch-based fusion framework for estimating accurate HR from face videos in the presence of subjects’ motions. The wavelet time–frequency analysis is applied on the raw blood volume pulse (BVP) signals for selecting less contaminated patches. Furthermore, a weighted fusion formula is constructed to obtain the final precise BVP signal, which is based on frequency and gradient information. Our method is validated on both our self-collected dataset and public dataset MAHNOB-HCI. Compared with the state of the art, experimental results show that the proposed method has an obvious superiority in the accuracy and robustness.
Similar content being viewed by others
References
Kannel, W.B., Kannel, C., Paffenbarger, R.S., Cupples, L.A.: Heart rate and cardiovascular mortality: the Framingham study. Am. Heart J. 113(6), 1489–1494 (1953). https://doi.org/10.1016/0002-8703(87)90666-1
Temko, A.: Accurate heart rate monitoring during physical exercises using PPG. IEEE Trans. Biomed. Eng. 64(9), 2016–2024 (2017). https://doi.org/10.1109/TBME.2017.2676243
Guven, G., Gurkan, H., Guz, U.: Biometric identification using fingertip electrocardiogram signals. Signal Image Video Process. 12, 1–8 (2018). https://doi.org/10.1007/s11760-018-1238-4
Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3), R1-39 (2007). https://doi.org/10.1088/0967-3334/28/3/R01
Wu, T., Blazek, V., Schmitt, H.: Photoplethysmography imaging: a new noninvasive and non-contact method for mapping of the dermal perfusion changes. Proc. SPIE 4163, 62–70 (2000). https://doi.org/10.1117/12.407646
Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16(26), 21434–21445 (2008). https://doi.org/10.1364/OE.16.021434
Poh, M.Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18(10), 10762–10774 (2010). https://doi.org/10.1364/OE.18.010762
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). San Diego, CA, USA, pp. 2174–2177 (2012). https://doi.org/10.1109/EMBC.2012.6346392
Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in non-contact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011). https://doi.org/10.1109/TBME.2010.2086456
Li, X.B., Chen, J., Zhao, G., Pietikainen, M.: Remote heart rate measurement from face videos under realistic situation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus, OH, USA, pp. 4264–4271 (2014). https://doi.org/10.1109/CVPR.2014.543
De Haan, G., Jeanne, V.: Robust pulse rate from chrominance-based rPPG. IEEE Trans. Biomed. Eng. 60(10), 2878–2886 (2013). https://doi.org/10.1109/TBME.2013.2266196
Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Algorithmic principles of remote-PPG. IEEE Trans. Biomed. Eng. 64(7), 1479–1491 (2017). https://doi.org/10.1109/TBME.2016.2609282
Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Amplitude-selective filtering for remote-PPG. Biomed. Opt. Express 8(3), 1965–1980 (2017). https://doi.org/10.1364/BOE.8.001965
Lam, A., Kuno, Y.: Robust heart rate measurement from video using select random patches. In: IEEE International Conference on Computer Vision (ICCV). Washington, DC, USA, pp. 3640–3648 (2015). https://doi.org/10.1109/ICCV.2015.415
Kumar, M., Veeraraghavan, A., Sabharwal, A.: DistancePPG: robust non-contact vital signs monitoring using a camera. Biomed. Opt. Express 6(5), 1565–1588 (2015). https://doi.org/10.1364/BOE.6.001565
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: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, pp. 2396–2404 (2016). https://doi.org/10.1109/CVPR.2016.263
Ha, R.Y., Nojima, K., Adams, W.J., Brown, S.A.: Analysis of facial skin thickness: defining the relative thickness index. Plast. Reconstr. Surg. 115(6), 1769–1773 (2005). https://doi.org/10.1097/01.PRS.0000161682.63535.9B
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Kauai, HI, USA, pp. 511–518 (2001). https://doi.org/10.1109/CVPR.2001.990517
Asthana, A., Zafeiriou, S., Cheng, S., Pantic, M.: Robust discriminative response map fitting with constrained local models. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Portland, OR, USA, pp. 3444–3451 (2013). https://doi.org/10.1109/CVPR.2013.442
Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical Report CMU-CS-91-132, Carnegie Mellon University (1991)
Daubechies, I., Heil, C.: Ten Lectures on Wavelets. Capital City Press, Vermont (1992)
Tarvainen, M.P., Ranta-aho, P.O., Karjalainen, P.A.: An advanced detrending method with application to HRV analysis. IEEE Trans. Biomed. Eng. 49(2), 172–175 (2002). https://doi.org/10.1109/10.979357
Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1), 42–55 (2012). https://doi.org/10.1109/T-AFFC.2011.25
Huang, S.J., Hsieh, C.T., Huang, C.L.: Application of Morlet wavelets to supervise power system disturbances. IEEE Trans. Power Deliv. 14(1), 235–243 (1999). https://doi.org/10.1109/61.736728
Acknowledgements
We acknowledge funding support from: Training Programme Foundation for Application of Scientific and Technological Achievements of Hefei University of Technology (JZ2018YYPY0289) and the Specialized Research Fund for the Doctoral Program of Higher Education of China (JZ2018HGBZ0186).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, Z., Yang, X., Jin, J. et al. Motion-resistant heart rate measurement from face videos using patch-based fusion. SIViP 13, 423–430 (2019). https://doi.org/10.1007/s11760-018-01409-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-018-01409-w