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Interoperability Among Capture Devices for Fingerprint Presentation Attacks Detection

  • Pierliugi TuveriEmail author
  • L. Ghiani
  • Mikel Zurutuza
  • V. Mura
  • G. L. Marcialis
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

A fingerprint verification system is vulnerable to attacks led through the fingertip replica of an enrolled user. The countermeasure is a software/hardware module called fingerprint presentation attacks detector (FPAD) that is able to detect images coming from a real (live) and a spoof (fake) fingertip. We focused our work on the so-called software-based solutions that use a classifier trained with a collection of live and fake fingerprint images in order to determine the liveness level of a finger, that is, the probability that the submitted fingerprint image is not a replica. The chapter goal is to give an overview of FPAD systems by focusing on the problem of the interoperability among different capture devices. In other words, the FPAD performance variation arises when the capture device is substituted by another one, for example, due to upgrading reasons. After a brief summary of the main and most effective state-of-the-art approaches to feature extraction, we introduce the interoperability FPAD problem from the image captured by the fingerprint sensor to the impact on the related feature space and classifier. In particular, we take into account the so-called textural descriptors used for FPAD. We review the state of the art in order to see if and how this problem has been already treated. Finally, a possible solution is suggested and a set of experiments is done to investigate its effectiveness.

References

  1. 1.
    Erdoğmuş N, Marcel S (2014) Introduction, pp 1–11. Springer, London.  https://doi.org/10.1007/978-1-4471-6524-8_1CrossRefGoogle Scholar
  2. 2.
    Willis D, Lee M (1998) Six biometric devices point the finger at security. Netw Comput 9(10):84–96 (1998). URL http://dl.acm.org/citation.cfm?id=296195.296211
  3. 3.
    van der Putte T, Keuning J (2001) Biometrical fingerprint recognition: don’t get your fingers burned. In: Proceedings of the fourth working conference on smart card research and advanced applications on smart card research and advanced applications. Kluwer Academic Publishers, Norwell, pp 289–303. http://dl.acm.org/citation.cfm?id=366214.366298CrossRefGoogle Scholar
  4. 4.
    Matsumoto T, Matsumoto H, Yamada K, Hoshino S (2002) Impact of artificial “gummy” fingers on fingerprint systems, vol 26Google Scholar
  5. 5.
    Schuckers SA (2002) Spoofing and anti-spoofing measures. Inf Sec Tech Rep 7(4):56–62.  https://doi.org/10.1016/S1363-4127(02)00407-7CrossRefGoogle Scholar
  6. 6.
    Yambay D, Ghiani L, Denti P, Marcialis GL, Roli F, Schuckers S (2012) Livdet 2011 - fingerprint liveness detection competition 2011. In: 2012 5th IAPR international conference on biometrics (ICB). IEEE, pp 208–215Google Scholar
  7. 7.
    Mura V, Ghiani L, Marcialis GL, Roli F, Yambay DA, Schuckers SA (2015) Livdet 2015 fingerprint liveness detection competition 2015. In: 2015 IEEE 7th international conference on biometrics theory, applications and systems (BTAS). IEEE, pp 1–6Google Scholar
  8. 8.
    Jain AK, Flynn P, Ross AA (eds) Handbook of biometrics. Springer (2008).  https://doi.org/10.1007/978-0-387-71041-9Google Scholar
  9. 9.
    Marasco E, Ross A (2014) A survey on antispoofing schemes for fingerprint recognition systems. ACM Comput Surv 47(2):28:1–28:36.  https://doi.org/10.1145/2617756CrossRefGoogle Scholar
  10. 10.
    Sousedik C, Busch C (2014) Presentation attack detection methods for fingerprint recognition systems: a survey. IET Biometr 3(4):219–233.  https://doi.org/10.1049/iet-bmt.2013.0020CrossRefGoogle Scholar
  11. 11.
    Coli P, Marcialis GL, Roli F (2007) Vitality detection from fingerprint images: a critical survey. In: Advances in biometrics: international conference, ICB 2007, Seoul, Korea, August 27–29, 2007. Proceedings. Springer, Berlin, pp 722–731.  https://doi.org/10.1007/978-3-540-74549-5-76
  12. 12.
    Derakhshani R, Schuckers S, Hornak LA, O’Gorman L (2003) Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners. Pattern Recogn 36:383–396CrossRefGoogle Scholar
  13. 13.
    Parthasaradhi STV, Derakhshani R, Hornak LA, Schuckers SAC (2005) Time-series detection of perspiration as a liveness test in fingerprint devices. IEEE Trans Syst Man Cybern Part C (Appl Rev) 35(3):335–343.  https://doi.org/10.1109/TSMCC.2005.848192CrossRefGoogle Scholar
  14. 14.
    Coli P, Marcialis GL, Roli F (2006) Analysis and selection of features for the fingerprint vitality detection. Springer, Berlin, pp 907–915.  https://doi.org/10.1007/11815921-100
  15. 15.
    Jia J, Cai L, Zhang K, Chen D (2007) A new approach to fake finger detection based on skin elasticity analysis. Springer, Berlin, pp 309–318.  https://doi.org/10.1007/978-3-540-74549-5_33
  16. 16.
    Antonelli A, Cappelli R, Maio D, Maltoni D (2006) Fake finger detection by skin distortion analysis. IEEE Trans Inf Forensics Sec 1(3):360–373.  https://doi.org/10.1109/TIFS.2006.879289CrossRefGoogle Scholar
  17. 17.
    Aditya Shankar Abhyankar SCS (2004) A wavelet-based approach to detecting liveness in fingerprint scanners. pp 5404 – 5404 – 9 (2004).  https://doi.org/10.1117/12.542939
  18. 18.
    Schuckers S, Abhyankar A (2004) Detecting liveness in fingerprint scanners using wavelets: results of the test dataset. Springer, Berlin, pp 100–110.  https://doi.org/10.1007/978-3-540-25976-3_10CrossRefGoogle Scholar
  19. 19.
    Zhang Y, Tian J, Chen X, Yang X, Shi P (2007) Fake finger detection based on thin-plate spline distortion model. Springer, Berlin, pp 742–749.  https://doi.org/10.1007/978-3-540-74549-5_78
  20. 20.
    Tan B, Schuckers S (2006) Liveness detection for fingerprint scanners based on the statistics of wavelet signal processing. In: 2006 conference on computer vision and pattern recognition workshop (CVPRW’06), pp 26–26 (2006).  https://doi.org/10.1109/CVPRW.2006.120
  21. 21.
    Tan B, Schuckers SAC (2008) New approach for liveness detection in fingerprint scanners based on valley noise analysis 17(011):009Google Scholar
  22. 22.
    Nikam SB, Agarwal S (2008) Fingerprint anti-spoofing using ridgelet transform. In: 2008 IEEE second international conference on biometrics: theory, applications and systems, pp 1–6.  https://doi.org/10.1109/BTAS.2008.4699347
  23. 23.
    Nikam SB, Agarwal S (2008) Fingerprint liveness detection using curvelet energy and co-occurrence signatures. 2008 fifth international conference on computer graphics, imaging and visualisation, pp 217–222Google Scholar
  24. 24.
    Nikam SB, Agarwal S (2010) Curvelet-based fingerprint anti-spoofing. Signal, Image Video Process 4(1):75–87.  https://doi.org/10.1007/s11760-008-0098-8CrossRefGoogle Scholar
  25. 25.
    Nikam SB, Agarwal S (2008) Texture and wavelet-based spoof fingerprint detection for fingerprint biometric systems. In: 2008 first international conference on emerging trends in engineering and technology, pp 675–680.  https://doi.org/10.1109/ICETET.2008.134
  26. 26.
    Nikam SB, Agarwal S (2008) Wavelet energy signature and glcm features-based fingerprint anti-spoofing. In: 2008 international conference on wavelet analysis and pattern recognition, vol 2, pp 717–723.  https://doi.org/10.1109/ICWAPR.2008.4635872
  27. 27.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621 (1973).  https://doi.org/10.1109/TSMC.1973.4309314CrossRefGoogle Scholar
  28. 28.
    Tan B, Schuckers S (2010) Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise. Pattern Recogn 43(8):2845–2857.  https://doi.org/10.1016/j.patcog.2010.01.023CrossRefzbMATHGoogle Scholar
  29. 29.
    Moon YS, Chen JS, Chan KC, So K, Woo KC (2005) Wavelet based fingerprint liveness detection. Electron Lett 41(20):1112–1113.  https://doi.org/10.1049/el:20052577CrossRefGoogle Scholar
  30. 30.
    Chen Y, Jain A, Dass S (2005) Fingerprint deformation for spoof detection. In: Biometric symposiumGoogle Scholar
  31. 31.
    Choi H, Kang R, Choi K, Kim J (2007) Aliveness detection of fingerprints using multiple static features. World academy of science, engineering and technology, vol 2Google Scholar
  32. 32.
    Abhyankar A, Schuckers S (2006) Fingerprint liveness detection using local ridge frequencies and multiresolution texture analysis techniques. In: 2006 international conference on image processing, pp 321–324.  https://doi.org/10.1109/ICIP.2006.313158
  33. 33.
    Marcialis GL, Roli F, Tidu A (2010) Analysis of fingerprint pores for vitality detection. In: 2010 20th international conference on pattern recognition, pp 1289–1292.  https://doi.org/10.1109/ICPR.2010.321
  34. 34.
    Marasco E, Sansone C (2010) An anti-spoofing technique using multiple textural features in fingerprint scanners. In: 2010 IEEE workshop on biometric measurements and systems for security and medical applications, pp 8–14.  https://doi.org/10.1109/BIOMS.2010.5610440
  35. 35.
    Galbally J, Alonso-Fernandez F, Fierrez J, Ortega-Garcia J (2012) A high performance fingerprint liveness detection method based on quality related features. Future Gener Comput Syst 28(1):311–321. http://dx.doi.org/10.1016/j.future.2010.11.024CrossRefGoogle Scholar
  36. 36.
    Gottschlich C, Marasco E, Yang AY, Cukic B (2014) Fingerprint liveness detection based on histograms of invariant gradients. In: IEEE international joint conference on biometrics, pp 1–7.  https://doi.org/10.1109/BTAS.2014.6996224
  37. 37.
    Marasco E, Wild P, Cukic B (2016) Robust and interoperable fingerprint spoof detection via convolutional neural networks. In: 2016 IEEE symposium on technologies for homeland security (HST), pp 1–6.  https://doi.org/10.1109/THS.2016.7568925
  38. 38.
    Frassetto Nogueira R, Lotufo R, Machado R (2016) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensics Sec 11:1–1CrossRefGoogle Scholar
  39. 39.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987.  https://doi.org/10.1109/TPAMI.2002.1017623CrossRefzbMATHGoogle Scholar
  40. 40.
    Mäenpää T, Pietikäinen M (2005) Texture analysis with local binary patterns. Handbook of pattern recognition and computer vision 3:197–216CrossRefGoogle Scholar
  41. 41.
    Heikkila J, Ojansivu V (2009) Methods for local phase quantization in blur-insensitive image analysis. In: International workshop on local and non-local approximation in image processing, 2009. LNLA 2009. IEEE, pp 104–111Google Scholar
  42. 42.
    Ghiani L, Marcialis GL, Roli F (2012) Fingerprint liveness detection by local phase quantization. In: 2012 21st international conference on pattern recognition (ICPR), pp 537–540Google Scholar
  43. 43.
    Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2010) Wld: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720CrossRefGoogle Scholar
  44. 44.
    Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2013) Fingerprint liveness detection based on weber local image descriptor. In: 2013 IEEE workshop on biometric measurements and systems for security and medical applications, pp 46–50.  https://doi.org/10.1109/BIOMS.2013.6656148
  45. 45.
    Ghiani L, Hadid A, Marcialis GL, Roli F (2013) Fingerprint liveness detection using binarized statistical image features. In: 2013 IEEE sixth international conference on biometrics: theory, applications and systems (BTAS), pp 1–6.  https://doi.org/10.1109/BTAS.2013.6712708
  46. 46.
    Kannala J, Rahtu E (2012) Bsif: Binarized statistical image features. In: 21st international conference on pattern recognition (ICPR) 2012. IEEE, pp 1363–1366Google Scholar
  47. 47.
    Jia X, Yang X, Zang Y, Zhang N, Dai R, Tian J, Zhao J (2013) Multi-scale block local ternary patterns for fingerprints vitality detection. In: 2013 international conference on biometrics (ICB), pp 1–6.  https://doi.org/10.1109/ICB.2013.6612964
  48. 48.
    Jia X, Yang X, Cao K, Zang Y, Zhang N, Dai R, Zhu X, Tian J (2014) Multi-scale local binary pattern with filters for spoof fingerprint detection. Inf Sci 268:91–102.  https://doi.org/10.1016/j.ins.2013.06.041. http://www.sciencedirect.com/science/article/pii/S0020025513004787. (New sensing and processing technologies for hand-based biometrics authentication)CrossRefGoogle Scholar
  49. 49.
    Krizhevsky A, Sutskever I, Hinton G.E (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. Curran Associates, Inc., pp 1097–1105. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
  50. 50.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1–9.  https://doi.org/10.1109/CVPR.2015.7298594
  51. 51.
    Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 539–546.  https://doi.org/10.1109/CVPR.2005.202
  52. 52.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR (2014). http://arxiv.org/abs/1409.1556
  53. 53.
    Nogueira RF, de Alencar Lotufo R, Machado RC (2014) Evaluating software-based fingerprint liveness detection using convolutional networks and local binary patterns. In: 2014 IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS) Proceedings, pp 22–29.  https://doi.org/10.1109/BIOMS.2014.6951531
  54. 54.
    Ghiani L, Mura V, Tuveri P, Marcialis GL (2017) On the interoperability of capture devices in fingerprint presentation attacks detectionGoogle Scholar
  55. 55.
    Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition, 2nd edn. Springer Publishing Company, Incorporated, BerlinCrossRefGoogle Scholar
  56. 56.
    Watson CI, Garris MD, Tabassi E, Wilson CL, Mccabe RM, Janet S, Ko K, User’s guide to nist biometric image software (nbis)Google Scholar
  57. 57.
    Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27CrossRefGoogle Scholar
  58. 58.
    Ghiani L, Yambay DA, Mura V, Marcialis GL, Roli, F, Schuckers SAC (2017) Review of the fingerprint liveness detection (livdet) competition series. Image Vis Comput 58(C):110–128 (2017).  https://doi.org/10.1016/j.imavis.2016.07.002CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pierliugi Tuveri
    • 1
    Email author
  • L. Ghiani
    • 1
  • Mikel Zurutuza
    • 1
  • V. Mura
    • 1
  • G. L. Marcialis
    • 1
  1. 1.Department of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly

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