Skip to main content
SpringerLink
Log in

Presentation Attack Detection for Mobile Device-Based Iris Recognition

  • Conference paper
  • First Online:
Image Processing and Communications (IP&C 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1062))

Included in the following conference series:

  • 652 Accesses

Abstract

Apart from ensuring high recognition accuracy, one of the main challenges associated with mobile iris recognition is reliable Presentation Attack Detection (PAD). This paper proposes a method of detecting presentation attacks when the iris image is collected in visible light using mobile devices. We extended the existing database of 909 bona-fide iris images acquired with a mobile phone by collecting additional 900 images of irises presented on a color screen. We explore different image channels in both RGB and HSV color spaces, deep learning-based and geometric model-based image segmentation, and use Local Binary Patterns (LBP) along with the selected statistical images features classified by the Support Vector Machine to propose an iris PAD algorithm suitable for mobile iris recognition setups. We found that the red channel in the RGB color space offers the best-quality input samples from the PAD point of view. In subject-disjoint experiments, this method was able to detect 99.78% of screen presentations, and did not reject any live sample.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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.

    Available for download at http://zbum.ia.pw.edu.pl/EN/node/46.

  2. 2.

    This dataset of attack iris samples is available to researchers at http://zbum.ia.pw.edu.pl/EN/node/46.

References

  1. Quinn, G.W., Matey, J.R., Grother, P.: IREX IX Part One: Performance of Iris Recognition Algorithms. In: NIST Interagency Report 8207 (2018). https://doi.org/10.6028/NIST.IR.8207

  2. International Telecommunication Union (ITU): Global and regional ICT data: mobile-cellular subscriptions. https://www.itu.int/en/itud/statistics/pages/stat/default.aspx. Accessed June 2018

  3. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623

    Article  MATH  Google Scholar 

  4. Trokielewicz, M., Czajka, A., Maciejewicz, P.: Presentation attack detection for cadaver iris. In: 9th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2018), Los Angeles, California, 22–25 October 2018 (2018)

    Google Scholar 

  5. Othman, N., Dorizzi, B., Garcia-Salicetti, S.: OSIRIS: an open source iris recognition software. https://doi.org/10.1016/j.patrec.2015.09.002

    Article  Google Scholar 

  6. Trokielewicz, M., Czajka, A., Maciejewicz, P.: Post-mortem iris recognition with deep-learning-based image segmentation (2019). http://arxiv.org/abs/1901.01708

  7. ISO/IEC 30107-1:2016: Information technology – biometric presentation attack detection – part 1: framework (2016)

    Google Scholar 

  8. Galbally, J., Marcel, S., Fierrez, J.: Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans. Image Process. 23(2), 710–724 (2014)

    Article  MathSciNet  Google Scholar 

  9. Ren, J., Jiang, X., Yuan, J.: Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans. Image Process. 22(10) (2013). https://doi.org/10.1109/TIP.2013.2268976

    Article  MathSciNet  Google Scholar 

  10. Bartuzi, E., Roszczewska, K., Trokielewicz, M., Białobrzeski, R.: MobiBits: multimodal mobile biometric database. In: 17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018, Darmstadt, Germany, 26–29 September 2018 (2018). https://doi.org/10.23919/BIOSIG.2018.8553108

  11. ISO/IEC JTC 1/SC 37: Information technology “vocabulary” part 37: biometrics (FDIS), October 2016

    Google Scholar 

  12. Czajka, A.: Pupil dynamics for iris liveness detection. IEEE Trans. Inf. Forensics Secur. 10(4), 726–735 (2015)

    Article  Google Scholar 

  13. Czajka, A., Bowyer, K.: Presentation attack detection for iris recognition: an assessment of the state of the art. ACM Comput. Surv. (in Rev.) (2018). https://arxiv.org/abs/1804.00194

  14. Doyle, J.S., Flynn, P.J., Bowyer, K.W.: Automated classification of contact lens type in iris images. In: IEEE International Conference on Biometrics (ICB), pp. 1–6, June 2013

    Google Scholar 

  15. Komulainen, J., Hadid, A., Pietikäinen, M.: Generalized textured contact lens detection by extracting BSIF description from Cartesian iris images. In: IEEE International Joint Conference on Biometrics (IJCB), pp. 1–7, September 2014

    Google Scholar 

  16. Menotti, D., Chiachia, G., Pinto, A., Schwartz, W.R., Pedrini, H., Falcão, A.X., Rocha, A.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015)

    Article  Google Scholar 

  17. Pala, F., Bhanu, B.: Iris liveness detection by relative distance comparisons. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017

    Google Scholar 

  18. Raghavendra, R., Busch, C.: Presentation attack detection on visible spectrum iris recognition by exploring inherent characteristics of light field camera. In: IEEE International Joint Conference on Biometrics, pp. 1–8, September 2014

    Google Scholar 

  19. Raghavendra, R., Raja, K.B., Busch, C.: ContlensNet: robust iris contact lens detection using deep convolutional neural networks. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1160–1167, March 2017

    Google Scholar 

  20. Raja, K.B., Raghavendra, R., Busch, C.: Video presentation attack detection in visible spectrum iris recognition using magnified phase information. IEEE Trans. Inf. Forensics Secur. 10(10), 2048–2056 (2015)

    Article  Google Scholar 

  21. Sequeira, A.F., Thavalengal, S., Ferryman, J., Corcoran, P., Cardoso, J.S.: A realistic evaluation of iris presentation attack detection. In: International Conference on Telecommunications and Signal Processing (TSP), pp. 660–664, June 2016

    Google Scholar 

  22. Thavalengal, S., Nedelcu, T., Bigioi, P., Corcoran, P.: Iris liveness detection for next generation smartphones. IEEE Trans. Consum. Electron. 62(2), 95–102 (2016)

    Article  Google Scholar 

Download references

Acknowledgments

Project CYBERSECIDENT/382354/II/NCBR/2018 financed by the National Centre for Research and Development in the framework of CyberSecIdent programme.

Author information

Authors and Affiliations

  1. Research and Academic Computer Network (NASK), Kolska 12, 01-045, Warsaw, Poland

    Ewelina Bartuzi & Mateusz Trokielewicz

Authors
  1. Ewelina Bartuzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mateusz Trokielewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mateusz Trokielewicz .

Editor information

Editors and Affiliations

  1. Institute of Telecommunications and Computer Science, University of Science and Technology (UTP), Bydgoszcz, Poland

    Michał Choraś

  2. Department of Telecommunications, Computer Sciences and Electrical Engineering, University of Science and Technology (UTP), Bydgoszcz, Poland

    Ryszard S. Choraś

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bartuzi, E., Trokielewicz, M. (2020). Presentation Attack Detection for Mobile Device-Based Iris Recognition. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications. IP&C 2019. Advances in Intelligent Systems and Computing, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-31254-1_5

Download citation

Publish with us

Policies and ethics

Search

Navigation