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CLNet: a contactless fingerprint spoof detection using deep neural networks with a transfer learning approach

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Abstract

Biometric fingerprint verification and identification have been extensively used in real life applications as an authentication and access control mechanism. Newer contactless fingerprint scanning technology offers high convenience and hygiene, especially in the view of COVID-19. Attackers still challenge the biometric security offered by contactless scanners by illegitimate acquisition of the user’s fingerprint through various spoofing methods. Therefore, detection of contactless fingerprint spoof is on the urge to protect the biometric security systems. The existing solutions to contactless fingerint spoof detection face the lacuna of considering limited fingerprint features leading to low spoof detection accuracy. In this study, this issue has been addressed and CLNet (Contact Less Network) approach is proposed to detect the spoofness in contactless fingerprints. The proposed CLNet is a deep neural network approach utilizing contactless fingerprint images followed by a transfer learning approach called SpoofDetNet which is based on the MobileNetV2 model. The motivation for the development of the SpoofDetNet is to create a spoof detection method viable for contactless fingerprint images as well as contact-based fingerprint images which stand strong among state-of-the-art models. We created a Spoofed-Contactless Adult Fingerprint (S-CLAF) dataset with live and spoof contactless fingerprint images. The CLNet approach was trained and tested on S-CLAF dataset and it achieved an accuracy of 99.07% across all spoofed materials. Furthermore, the proposed approach was tested using LivDet 2015 benchmark dataset and IIT Bombay touchless fingerprint dataset achieving accuracy of 98.32% and 99.38% respectively. It is evident from the experimental results that the proposed CLNet outperforms the state-of-the-art fingerprint spoof detection methods.

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References

  1. Rayani PK, Changder S (2023) Continuous user authentication on smartphone via behavioral biometrics: a survey. Multimed Tools Appl 82(2):1633–1667

    Article  Google Scholar 

  2. Maheswari BU, Rajakumar M, Ramya J (2022) Dynamic differential annealing-based anti-spoofing model for fingerprint detection using cnn. Neural Comput Appl 34(11):8617–8633

    Article  Google Scholar 

  3. Amma NGN, Amma NGB (2022) Pclusba: A novel partition clusteringbased biometric authentication mechanism. IETE J Res, 1–6

  4. ISO/IEC30107-1 (2016) Information Technology–Biometric Presentation Attack Detection–Part 1: Framework, Standard ISO/IEC30107-1:2016, International Standards Organization. https://www.iso.org/standard/53227.html 03-Dec.-2021

  5. Arora SS, Cao K, Jain AK, Paulter NG (2016) Design and fabrication of 3d fingerprint targets. IEEE Trans Inf Forensic Secur 11(10):2284–2297

    Article  Google Scholar 

  6. Baldisserra D, Franco A, Maio D, Maltoni D (2006) Fake fingerprint detection by odor analysis. In: International Conference on Biometrics, p 265–272. Springer

  7. Li H, Ramachandra R (2023) Deep learning based fingerprint presentation attack detection: A comprehensive Survey. arXiv:2305.17522

  8. Biggio B, Akhtar Z, Fumera G, Marcialis GL, Roli F (2012) Security evaluation of biometric authentication systems under real spoofing attacks. IET Biometrics 1(1):11–24

    Article  Google Scholar 

  9. Huang Q, Chang S, Liu C, Niu B, Tang M, Zhou Z (2015) An evaluation of fake fingerprint databases utilizing svm classification. Pattern Recogn Lett 60:1–7

    Article  ADS  Google Scholar 

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

    Article  Google Scholar 

  11. Sousedik C, Busch C (2014) Presentation attack detection methods for fingerprint recognition systems: a survey. Iet Biometrics 3(4):219–233

    Article  Google Scholar 

  12. Liu F, Zhao Y, Liu G, Shen L (2020) Fingerprint pore matching using deep features. Pattern Recogn 102:107208

    Article  Google Scholar 

  13. 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(2):264–273

    Google Scholar 

  14. Peralta D, Galar M, Triguero I, Paternain D, García S, Barrenechea E, Benítez JM, Bustince H, Herrera F (2015) A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation. Inf Sci 315:67–87

    Article  MathSciNet  Google Scholar 

  15. Liu F, Liu G, Zhao Q, Shen L (2020) Robust and high-security fingerprint recognition system using optical coherence tomography. Neurocomputing 402:14–28

    Article  Google Scholar 

  16. Galbally J, Beslay L, Böstrom G (2020) 3d-flare: A touchless full-3d fingerprint recognition system based on laser sensing. IEEE Access 8:145513–145534

    Article  Google Scholar 

  17. Agarwal R, Jalal A, Arya K (2020) A review on presentation attack detection system for fake fingerprint. Mod Phys Lett B 34(05):2030001

    Article  ADS  CAS  Google Scholar 

  18. Ishfaq R, Selwal A, Sharma D (2021) Fingerprint spoofing attacks and their deep learning-enabled remediation: State-of-the-art, taxonomy, and future directions. In: 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT), p 22–28. IEEE

  19. Chugh T, Jain AK (2020) Fingerprint spoof detector generalization. IEEE Trans Inf Forensic Secur 16:42–55

    Article  Google Scholar 

  20. Agarwal S, Chowdary CR (2020) A-stacking and a-bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection. Expert Syst Appl 146:113160

    Article  Google Scholar 

  21. Banerjee S, Chaudhuri S, et al (2020) Defraudnet: End2end fingerprint spoof detection using patch level attention. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, p 2695–2704

  22. Grosz SA, Chugh T, Jain AK (2020) Fingerprint presentation attack detection: A sensor and material agnostic approach. In: 2020 IEEE International Joint Conference on Biometrics (IJCB), p 1–10. IEEE

  23. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861

  24. Thentu S, Cordeiro R, Park Y, Karimian N (2021) Ecg biometric using 2d deep convolutional neural network. In: 2021 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–6. IEEE

  25. Tuesta-Monteza VA, Cespedes-Ordoñez BN, Mejia-Cabrera HI, Forero MG (2021) Development of a method for identifying people by processing digital images from handprint. In: Mexican Conference on Pattern Recognition, p 231–239. Springer

  26. Kolberg J, Gomez-Barrero M, Busch C (2020) On the generalisation capabilities of fingerprint presentation attack detection methods in the short wave infrared domain. arXiv:2010.09566

  27. Zhang Z, Liu S, Liu M (2021) A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction. Pattern Recogn 120:108189

    Article  Google Scholar 

  28. Tan H, Kumar A (2020) Towards more accurate contactless fingerprint minutiae extraction and pose-invariant matching. IEEE Trans Inf Forensic Secur 15:3924–3937

    Google Scholar 

  29. Dwivedi R, Pandya V, Shah P (2021) Touchless fingerprint recognition based on hierarchical clustering. In: 2021 International Conference on Machine Vision and Applications, pp. 8–13

  30. Menotti D, Chiachia G, Pinto A, Schwartz WR, Pedrini H, Falcao AX, Rocha A (2015) Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans Inf Forensic Secur 10(4):864–879

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Rehman YAU, Po LM, Liu M (2018) Livenet: Improving features generalization for face liveness detection using convolution neural networks. Expert Syst Appl 108:159–169

    Article  Google Scholar 

  33. 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 

  34. TK AK, Vinayakumar R, Sowmya V, Soman K (2019) Convolutional neural networks for fingerprint liveness detection system. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS), p 243–246. IEEE

  35. Jomaa MR, Mathkour H, Bazi Y, Islam MS (2020) End-to-end deep learning fusion of fingerprint and electrocardiogram signals for presentation attack detection. Sensors 20(7):2085

    Article  ADS  Google Scholar 

  36. Yuan C, Jiao S, Sun X, Wu QJ (2021) Mfffld: A multimodal-feature-fusionbased fingerprint liveness detection. IEEE Trans Cogn Dev Syst 14(2):648–661

    Article  Google Scholar 

  37. Garg H, Sharma B, Shekhar S, Agarwal R (2022) Spoofing detection system for e-health digital twin using efficientnet convolution neural network. Multimed Tools Appl 81(19):26873–26888

    Article  Google Scholar 

  38. Cheng X, Zhou J, Zhao X, Wang H, Li Y (2023) A presentation attack detection network based on dynamic convolution and multi-level feature fusion with security and reliability. Futur Gener Comput Syst 146:114–121

    Article  Google Scholar 

  39. Sasi NM, Jayasree V et al (2013) Contrast limited adaptive histogram equalization for qualitative enhancement of myocardial perfusion images. Engineering 5(10):326–331

    Article  Google Scholar 

  40. Moraru L, Obreja CD, Dey N, Ashour AS (2018) Dempster-shafer fusion for effective retinal vessels’ diameter measurement. In: Soft Computing Based Medical Image Analysis, p 149–160. Elsevier, ???

  41. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p 4510–4520 (2018)

  42. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p 1251–1258

  43. Ghiani L, Yambay DA, Mura V, Marcialis GL, Roli F, Schuckers SA (2017) Review of the fingerprint liveness detection (livdet) competition series: 2009 to 2015. Image Vis Comput 58:110–128

    Article  Google Scholar 

  44. Birajadar P, Haria M, Kulkarni P, Gupta S, Joshi P, Singh B, Gadre V (2019) Towards smartphone-based touchless fingerprint recognition. Sādhanā 44(7), 1–15

  45. Hadid A, Evans N, Marcel S, Fierrez J (2015) Biometrics systems under spoofing attack: an evaluation methodology and lessons learned. IEEE Signal Process Mag 32(5):20–30

    Article  ADS  Google Scholar 

  46. Nogueira RF, de Alencar Lotufo R, Machado RC (2016) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensic Secur 11(6):1206–1213

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Grand Challenges India (GCI) for Immunization Data funded by Biotechnology Industry Research Assistance Council (BIRAC) and jointly funded by Bill & Melinda Gates foundation under the project titled Nagarik Rog Pratirakshak: Unified Smart Immunization Coverage Monitoring and Analysis (NRP-UniSICMA).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, 603 110, Tamil Nadu, India

    Kanchana Rajaram

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600 127, Tamil Nadu, India

    Bhuvaneswari Amma N.G. & Ashwin S. Guptha

  3. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, 620 015, Tamil Nadu, India

    Selvakumar S.

  4. Indian Institute of Information Technology, Una, 177 209, Himachal Pradesh, India

    Selvakumar S.

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  1. Kanchana Rajaram
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  2. Bhuvaneswari Amma N.G.
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  3. Ashwin S. Guptha
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  4. Selvakumar S.
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Correspondence to Kanchana Rajaram.

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Rajaram, K., N.G., B.A., Guptha, A.S. et al. CLNet: a contactless fingerprint spoof detection using deep neural networks with a transfer learning approach. Multimed Tools Appl 83, 27703–27722 (2024). https://doi.org/10.1007/s11042-023-16511-6

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  • DOI: https://doi.org/10.1007/s11042-023-16511-6

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