Skip to main content
SpringerLink
Log in

Fingerprint and Iris liveness detection using invariant feature-set

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Presentation attacks that make the biometric systems vulnerable has become a growing concern in recent years keeping in view its widespread applications in the field of banking, medical, security systems etc. For instance, textured contact lenses, high-quality printouts and fabricated synthetic materials spoof the iris texture and fingerprints that lead to increase in false rejection. Till now, extensive work has been done on global features. However, this paper proposed local features with invariance properties. Thus, the paper proposes detection of spoofing attacks in which local features are extracted for micro-textural analysis with properties of invariance to scale, rotation and translation. The features are encoded using Lehmer code and transformed into histograms that act as feature descriptors for classification. The top 4 features are selected using Friedman test. Experiments are simulated on iris spoofing databases: IIITD-Contact Lens, IIITD-Iris Spoofing, Clarkson-2015, Warsaw-2015and fingerprint spoofing databases: LivDet-2013 and LivDet-2015. Results have been validated through intra-sensor, inter-sensor, cross-sensor and cross-material. In case of IIITD-CLI, an EER of 1.36% and an ACER of 1.45% is obtained. For IIS, 0.94% of EER and 1.61% of ACER is observed. For Clarkson database, 0.79% of EER and 2.10% of ACER is obtained. An ACER of 0.57% is obtained for LivDet-2013 and 0.47% for LivDet-2015.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Antonelli A, Cappelli R, Maio D, Maltoni D (2006) Fake finger detection by skin distortion analysis. IEEE Trans Inf Forensics Secur 1(3):360–373

    Article  Google Scholar 

  2. Arndt J (2011) Matters Computational- Ideas, Algorithms, Source Code. Springer

    Book  Google Scholar 

  3. Baldisserra D, Franco A, Maio D, Maltoni D (2006) Fake fingerprint detection by odor analysis. Springer, pp 265–272

    Google Scholar 

  4. Chugh T, Cao K, Jain AK (2018) Fingerprint spoof buster: use of minutiae-centered patches. IEEE Trans Inf Forensics Secur 13(9):2190–2202

    Article  Google Scholar 

  5. Chugh T, Jain AK (2021) Fingerprint spoof detector generalization. IEEE Trans Inf Forensics Secur 16:42–55

    Article  Google Scholar 

  6. Contreras RC et al (2022) A new multi-filter framework for texture image representation improvement using set of pattern descriptors to fingerprint liveness detection. IEEE Access 10:117681–117706

    Article  Google Scholar 

  7. Daugman J (2004) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14(1):21–30

    Article  Google Scholar 

  8. Ding Y, Ross A (2016) An ensemble of one-class SVMs for fingerprint spoof detection across different fabrication materials. Proc. IEEE International Workshop on Information Forensics and Security, 1–6

  9. Dong Y, Tao D, Li X, Ma J, Pu J (2015) Texture classification and retrieval using shearlets and linear regression. IEEE Trans Cybern 45(3):358–369

    Article  Google Scholar 

  10. Dubey RK, Goh J, Thing VLL (2016) Fingerprint liveness detection from single image using low-level features and shape analysis. IEEE Trans Inf Forensics Secur 11(7):1461–1475

    Article  Google Scholar 

  11. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Amer Statist Assoc 32(200):675–701

    Article  Google Scholar 

  12. Galbally J, Alonso-Fernandez F, Fierrez J, Ortega-Garcia J (2009) Fingerprint liveness detection based on quality measures. Int. Conference on Biometrics, Identity and Security (BIDS), 1–8

  13. Ghiani L, Marcialis GL, Roli F (2012) Fingerprint liveness detection by local phase quantization. Proc. 21st Int. Conf. Pattern Recognit. (ICPR), 537–540

  14. Ghiani L, Hadid A, Marcialis G, Roli F (2013) Fingerprint liveness detection using binarized statistical image features. IEEE Int. Conference on Biometrics: Theory Applications and Systems (VISAPP), 1–6

  15. Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2013) Fingerprint liveness detection based on weber local image descriptor. IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS), 46–50

  16. Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) An investigation of local descriptors for biometric spoofing detection. IEEE Trans Inf Forensics Secur 10(4):849–863

    Article  Google Scholar 

  17. Gupta P, Behera S, Vatsa M, Singh R (2014) On iris spoofing using print attack. 22nd international conference on Pattern recognition (ICPR), 1681–1686

  18. González-Soler LJ, Gomez-Barrero M, Chang L, Pérez-Suárez A, Busch C (2019) Fingerprint presentation attack detection based on local features encoding for unknown attacks 1–14. [Online]. http://arxiv.org/abs/1908.101631. Accessed 28 Dec 2023

  19. Gottschlich C, Marasco E, Yang AY, Cukic B (2014) Fingerprint liveness detection based on histograms of invariant gradients. Biometrics (IJCB), 2014 IEEE International Joint Conference on, Clearwater, 1–7

  20. Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Informa Theory 8(2):179–187

    Article  Google Scholar 

  21. International Standards Organization (2016) ISO/IEC 30107-1:2016, Information technology-biometric presentation attack detection—Part 1: Framework. https://www.iso.org/standard/53227.html. Accessed 28 Dec 2023

  22. Jenkins J, Roy K, Shelton J (2020) Using deep learning techniques and genetic-based feature extraction forpresentation attack mitigation. Array 7:1–10

    Article  Google Scholar 

  23. Jia X, Yang X, Zang Y, Zhang N, Dai R, Tian J, Zhao J (2013) Multi-scale block local ternary patterns for fingerprint vitality detection. International Conference on Biometrics, 1–6

  24. Jian W, Zhou Y, Liu H (2021) Densely connected convolutional network optimized by genetic algorithm for fingerprint liveness detection. IEEE Access 9:2229–2243

    Article  Google Scholar 

  25. Jiang Y, Liu X (2018) Uniform local binary pattern for fingerprint liveness detection in the gaussian pyramid. J Electr Comput Eng 2018:1–9

    Article  Google Scholar 

  26. Kannala J, Rahtu E (2012) BSIF: Binarized statistical image features. Proc. 21st Int. Conf. Pattern Recognit, 1363–1366

  27. Karasu S, Altan A (2019). Recognition model for solar radiation time series based on random forest with feature selection approach. 11th International Conference on Electrical and Electronics Engineering (ELECO), 8–11

  28. Karasu S, Altan A, Bekiros S, Ahmad W (2020) A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy 212:1–12

    Article  Google Scholar 

  29. Kaur B (2020) Iris spoofing detection using discrete orthogonal moments. Multimed Tools Appl 79:6623–6647

    Article  Google Scholar 

  30. Kaur B (2023) Iris and Periocular Recognition using Shape Descriptors and Local Invariant Features. 2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON), 1–5

  31. Kokkinos I, Yuille A (2008) Scale invariant without scale selection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1–8

  32. Lapsley PD, Less JA, Pare DF Jr, Hoffman N (1998) Anti-fraud biometric sensor that accurately detects blood flow. SmartTouch, LLC, US Patent #5737439

  33. Lilhore UK, Poongodi M, Kaur A, Simaiya S, Algarni AD, Elmannai H, Vijayakumar V, Tunze GB, Hamdi M (2022) Hybrid model for detection of cervical cancer using causal analysis and machine learning techniques. Comput Math Methods Med 22:1–17

    Article  Google Scholar 

  34. Lowe D (2004) Distinctive image features from scale invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  35. Masek L (2003) Recognition of human iris patterns for biometric identification, 1–7. http://www.csse.uwa.edu.au/opk/studentprojects/labor. Accessed 28 Dec 2023

  36. Mukundan R, Lee PA (2001) Image analysis by tchebichef moments. IEEE Trans Image Process 10(9):1357–1364

    Article  MathSciNet  Google Scholar 

  37. Mura V, Ghiani L, Marcialis GL, Roli F, Yambay DA, Schuckers SA (2015) LivDet 2015 - Fingerprint liveness detection competition 2015. Proc. IEEE 7th Intl. Conf. BTAS, 1–6

  38. Nguyen T, Park E, Cui X, Nguyen V, Kim H (2018) FPADnet: Small and efficient convolutional neural network for presentation attack detection. Sensors 18(8):25–32

    Article  Google Scholar 

  39. Nogueira RF, Lotufo RDA, Machado RC (2015) Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns. In: IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications Proceedings, pp 22–29

  40. Nogueira RF, de AlencarLotufo R, Machado RC (2016) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf ForensicsSecur 11(6):1206–1213

    Article  Google Scholar 

  41. Ojala T, Pietikainen M, Maenpaa T (2001) A generalized local binary pattern operator for multi-resolution gray-scale and rotataion invariant texture classification. Proceedings of International Conference on Advances in Pattern Recognition, 397–406

  42. Ojala T, Pietikainen M, Maenpaa T (2002) Gray scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  43. Pala F, Bhanu B (2017) Deep triplet embedding representations for liveness detection. In: Bhanu B, Kumar A (eds) Deep learning for biometrics. Advances in computer vision and pattern recognition. Springer, Cham

  44. Raghavendra R, Raja KB, Busch C (2014) Ensemble of statistically independent filters for robust contact lens detection in iris images. Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing, 24

  45. Raghavendra R, Raja KB, Busch C (2017) ContlensNet: Robust iris contact lens detection using deep convolutional neural networks. IEEE Winter Conference on Applications of Computer Vision (WACV), 1160–1167

  46. Rattani A, Scheirer WJ, Ross A (2015) Open set fingerprint spoof detection across novel fabrication materials. IEEE Trans Inf Forensics Secur 10(11):2447–2460

    Article  Google Scholar 

  47. Rehman YAU, Po LM, Liu M (2020) SLNet: Stereo face liveness detection via dynamic disparity-maps and convolutional neural network. Expert Syst Appl 142:1–29

    Article  Google Scholar 

  48. Shukla PK, Sandhu JK, Ahirwar A, Ghai D, Maheshwary P, Shukla PK (2021) Multi-objective genetic algorithm and convolutional neural network based COVID-19 identification in chest X-ray images. Math Probl Eng 2021:1–9

    Article  Google Scholar 

  49. Silva P, Luz E, Baeta R, Pedrini H, Falcao AX, Menotti D (2015) An approach to iris contact lens detection based on deep image representations. 28th SIBGRAPI Conference onGraphics, Patterns and Images (SIBGRAPI),157–164

  50. Spinoulas L, Mirzaalian H, Hussein ME, AbdAlmageed W (2021) Multi-modal fingerprint presentation attack detection: evaluation on a new dataset. IEEE Trans Biom Behav Identity Sci 3(3):347–364

    Article  Google Scholar 

  51. Tapia JE, Gonzalez S, Busch C (2022) Iris liveness detection using a cascade of dedicated deep learning networks. IEEE Trans Inform Forensics Secur 17:42–52

    Article  Google Scholar 

  52. Teague MR (1980) Image analysis via the general theory of moments. J Optic Soc Am 70(8):920–930

    Article  MathSciNet  Google Scholar 

  53. Thenuwara SS, Premchandra C, Kawanaka H (2022) A multi-agent based enhancement for multimodal biometric system at border control. Array 14:1–11

    Article  Google Scholar 

  54. Tola E, Lepetit V, Fua P (2010) DAISY: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32(5):815–830

    Article  Google Scholar 

  55. Uliyan DM, Sadeghi S, Jalab HA (2020) Anti-spoofing method for fingerprint recognition using patch based deep learning machine. Eng Sci Technol Int J 23:264–273

    Google Scholar 

  56. Xia Z, Yuan C, Lv R, Sun X, Xiong NN, Shi YQ (2020) A novel Weber local binary descriptor for fingerprint liveness detection. IEEETrans Syst Man Cybern Syst 50(4):1526–1536

    Article  Google Scholar 

  57. Yadav D, Kohli N, Doyle JS, Singh R, Vatsa M, Bowyer KW (2014) Unraveling the effect of textured contact lenses on iris recognition. IEEE Trans Inf Forensics Secur 9(5):851–862

    Article  Google Scholar 

  58. Yambay D, Walczak B, Schuckers S, Czajka A (2017) LivDet-Iris 2015 - Iris Liveness Detection. IEEE International Conference on Identity, Security and Behavior Analysis, 1–6

  59. Yap PT, Raveendran P, Ong SH (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12(11):1367–1377

    Article  MathSciNet  Google Scholar 

  60. Yap PT, Raveendran P, Ong SH (2007) Image analysis using Hahn moments. IEEE Trans Pattern Anal Mach Intell 29(11):2057–2062

    Article  Google Scholar 

  61. Yap PT, Jiang XD, Chung AC (2010) Two-dimensional polar harmonic transforms for invariant image representation. IEEE Trans Pattern Anal Mach Intell 32(7):345–351

    Google Scholar 

  62. Yuan C, Xia Z, Sun X, Sun D, Lv R (2016) Fingerprint liveness detection using multiscale difference co-occurrence matrix. Opt Eng 55(6):063111-1-063111-10

    Article  Google Scholar 

  63. Yuan C, Xia Z, Sun X, Wu QMJ (2020) Deep residual network with adaptive learning framework for fingerprint liveness detection. IEEE Trans Cogn Dev Syst 12(3):461–473

    Article  Google Scholar 

  64. Yuan C, Yu P, Xia Z, Sun X, Wu QMJ (2022a) FLD-SRC: fingerprint liveness detection for AFIS based on spatial ridges continuity. IEEE J Sel Top Signal Process 16(4):817–827

    Article  Google Scholar 

  65. Yuan C, Jiao S, Sun X, Wu QMJ (2022b) MFFFLD: a multimodal-feature-fusion-based fingerprint liveness detection. IEEE Trans Cogn Dev Syst 14(2):648–661

    Article  Google Scholar 

  66. Zhang H, Sun Z, Tan T (2010) Contact lens detection based on weighted LBP. International Conference on Pattern Recognition (ICPR), 4279–4282

  67. Zhang Y, Shi D, Zhan X, Cao D, Zhu K, Li Z (2019) Slim-ResCNN: a deep residual convolutional neural network for fingerprint liveness detection. IEEE Access 7:91476–91487

    Article  Google Scholar 

  68. Zhang Y, Gao C, Pan S, Li Z, Xu Y, Qiu H (2020a) A score-level fusion of fingerprint matching with fingerprint liveness detection. IEEE Access 8:183391–183400

    Article  Google Scholar 

  69. Zhang Y, Pan S, Zhan X, Li Z, Gao M, Gao C (2020b) FLDNet: light dense CNN for fingerprint liveness detection. IEEE Access 8:84141–84152

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Bineet Kaur

Authors
  1. Bineet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bineet Kaur.

Ethics declarations

Conflict of interest

The author declares that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, B. Fingerprint and Iris liveness detection using invariant feature-set. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-17854-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-023-17854-w

Keywords

Search

Navigation