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Continuous Presentation Attack Detection in Face Biometrics Based on Heart Rate

  • Javier Hernandez-Ortega
  • Julian FierrezEmail author
  • Ester Gonzalez-Sosa
  • Aythami Morales
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11264)

Abstract

In this paper we study face Presentation Attack Detection (PAD) against realistic 3D mask and high quality photo attacks in dynamic scenarios. We perform a comparison between a new pulse-based PAD approach based on a combination of a skin detector and a chrominance method, and the system used in our previous works (based on Blind Source Separation techniques, BSS). We also propose and study heuristical and statistical approaches for performing continuous PAD with low latency and false non-match rate. Results are reported using the 3D Mask Attack Database (3DMAD), and a self-collected dataset called BiDA Heart Rate Database (BiDA HR) including different video durations, resolutions, frame rates and attack artifacts. Several conclusions can be drawn from this work: (1) chrominance and BSS methods perform similarly under the controlled and favorable conditions found in 3DMAD and BiDA HR, (2) combining pulse information extracted from short-time sequences (e.g. 3 s) can be discriminant enough for performing the PAD task, (3) a high increase in PAD performance can be achieved with simple PAD score combination, and (4) the statistical method for continuous PAD outperforms the simple PAD score combination but it needs more data for building the statistical models.

Keywords

Face Presentation Attack Detection Liveness detection Continuous authentication 

Notes

Acknowledgements

This work was supported in part by Accenture, project CogniMetrics from MINECO/FEDER under Grant TEC2015-70627-R, and project Neurometrics (CEALAL/2017-13) from UAM-Banco Santander. The work of J. Hernandez-Ortega was supported by a Ph.D. Scholarship from Universidad Autonoma de Madrid.

References

  1. 1.
    Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Measur. 28(3), R1–R39 (2007)CrossRefGoogle Scholar
  2. 2.
    Alonso-Fernandez, F., Fierrez, J., Ortega-Garcia, J.: Quality measures in biometric systems. IEEE Secur. Priv. 10(6), 52–62 (2012)Google Scholar
  3. 3.
    Bharadwaj, S., Dhamecha, T.I., Vatsa, M., Singh, R.: Computationally efficient face spoofing detection with motion magnification. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 105–110 (2013)Google Scholar
  4. 4.
    Dargie, W.: Analysis of time and frequency domain features of accelerometer measurements. In: International Conference on Computer Communication and Networks. IEEE (2009)Google Scholar
  5. 5.
    De Haan, G., Jeanne, V.: Robust pulse rate from chrominance-based rPPG. IEEE Trans. Biomed. Eng. 60(10), 2878–2886 (2013)CrossRefGoogle Scholar
  6. 6.
    Erdogmus, N., Marcel, S.: Spoofing face recognition with 3D masks. IEEE Trans. Inf. Forensics Secur. 9(7), 1084–1097 (2014)CrossRefGoogle Scholar
  7. 7.
    Fierrez, J., Pozo, A., Martinez-Diaz, M., Galbally, J., Morales, A.: Benchmarking touchscreen biometrics for mobile authentication. IEEE Trans. Inf. Forensics Secur. 13(11), 2720–2733 (2018)CrossRefGoogle Scholar
  8. 8.
    Fierrez, J., Morales, A., Vera-Rodriguez, R., Camacho, D.: Multiple classifiers in biometrics. Part 2: trends and challenges. Inf. Fusion 44, 103–112 (2018)CrossRefGoogle Scholar
  9. 9.
    Galbally, J., Gomez-Barrero, M., Ross, A.: Accuracy evaluation of handwritten signature verification: rethinking the random-skilled forgeries dichotomy. In: IEEE International Joint Conference on Biometrics (IJCB), pp. 302–310 (2017)Google Scholar
  10. 10.
    Hadid, A., Evans, N., Marcel, S., Fierrez, J.: Biometrics systems under spoofing attack: an evaluation methodology and lessons learned. IEEE Sig. Process. Mag. 32(5), 20–30 (2015)CrossRefGoogle Scholar
  11. 11.
    Hernandez-Ortega, J., Fierrez, J., Morales, A., Tome, P.: Time analysis of pulse-based face anti-spoofing in visible and NIR. In: IEEE CVPR Computer Society Workshop on Biometrics (2018)Google Scholar
  12. 12.
    Li, X., Komulainen, J., Zhao, G., Yuen, P.C., Pietikäinen, M.: Generalized face anti-spoofing by detecting pulse from face videos. In: International Conference on Pattern Recognition (ICPR), pp. 4244–4249. IEEE (2016)Google Scholar
  13. 13.
    Mahmoud, T.M., et al.: A new fast skin color detection technique. World Acad. Sci. Eng. Technol. 43, 501–505 (2008)Google Scholar
  14. 14.
    Marcel, S., Nixon, M.S., Fierrez, J., Evans, N.: Handbook of Biometric Anti-Spoofing, 2nd edn. Springer, Heidelberg (2019)CrossRefGoogle Scholar
  15. 15.
    McDuff, D., Gontarek, S., Picard, R.W.: Improvements in remote cardiopulmonary measurement using a five band digital camera. IEEE Trans. Biomed. Eng. 61(10), 2593–2601 (2014)CrossRefGoogle Scholar
  16. 16.
    Perera, P., Patel, V.M.: Efficient and low latency detection of intruders in mobile active authentication. IEEE Trans. Inf. Forensics Secur. 13(6), 1392–1405 (2018)CrossRefGoogle Scholar
  17. 17.
    Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011)CrossRefGoogle Scholar
  18. 18.
    Rapczynski, M., Werner, P., Al-Hamadi, A.: Continuous low latency heart rate estimation from painful faces in real time. In: International Conference on Pattern Recognition (ICPR), pp. 1165–1170 (2016)Google Scholar
  19. 19.
    Tasli, H.E., Gudi, A., den Uyl, M.: Remote PPG based vital sign measurement using adaptive facial regions. In: Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 1410–1414 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad Autonoma de MadridMadridSpain

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