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Adaptive thresholding pattern for fingerprint forgery detection

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Abstract

Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real ones. The software based techniques can detect the fingerprint forgery automatically. Also, the scheme shall be resistant against various distortions such as noise contamination, pixel missing and block missing, so that the forgers cannot deceive the detector by adding some distortions to the faked fingerprint. In this paper, we propose a fingerprint forgery detection algorithm based on a suggested adaptive thresholding pattern. The anisotropic diffusion of the input image is passed through three levels of the wavelet transform. The coefficients of different layers are adaptively thresholded and concatenated to produce the feature vector which is classified using the SVM classifier. Another contribution of the paper is to investigate the effect of various distortions such as pixel missing, block missing, and noise contamination. Our suggested approach includes a novel method that exhibits improved resistance against a range of distortions caused by environmental phenomena or manipulations by malicious users. In quantitative comparisons, our proposed method outperforms its counterparts by approximately 8% and 5% in accuracy for missing pixel scenarios of 90% and block missing scenarios of size \(70 \times 70\), respectively. This highlights the novelty approach in addressing such challenges.

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References

  1. Chugh T, Cao K, Jain AK (2017) Fingerprint spoof detection using minutiae-based local patches. In: 2017 IEEE international joint conference on biometrics (IJCB). IEEE, pp 581–589

  2. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639

    Article  Google Scholar 

  3. Ahamed BB, Yuvaraj D, Shitharth S, Mirza OM, Alsobhi A, Yafoz A et al (2022) An efficient mechanism for deep web data extraction based on tree-structured web pattern matching. Wireless Commun Mobile Comput 2022

  4. Sara A, Shitharth S, Sangeetha K, Reddy CVS et al (2022) Circuit manufacturing defect detection using VGG16 convolutional neural networks. Wireless Commun Mobile Comput

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

    Article  Google Scholar 

  6. Jia J, Cai L, Zhang K, Chen D (2007) A new approach to fake finger detection based on skin elasticity analysis. In: International conference on biometrics. Springer, pp 309–318

  7. Antonelli A, Cappelli R, Maio D, Maltoni D (2006) A new approach to fake finger detection based on skin distortion. In: International conference on biometrics. Springer, pp 221–228

  8. Zhang Y, Tian J, Chen X, Yang X, Shi P (2007) Fake finger detection based on thin-plate spline distortion model. In: International conference on biometrics. Springer, pp 742–749

  9. Parthasaradhi ST, Derakhshani R, Hornak LA, Schuckers SA (2005) Time-series detection of perspiration as a liveness test in fingerprint devices. IEEE Trans Syst Man Cybernet Part C (Appl Rev) 35(3):335–343

    Article  Google Scholar 

  10. Abhyankar A, Schuckers S (2009) Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recognit 42(3):452–464

    Article  ADS  Google Scholar 

  11. Nikam SB, Agarwal S (2009) Ridgelet-based fake fingerprint detection. Neurocomput 72(10–12):2491–2506

    Article  Google Scholar 

  12. Kulkarni SS, Patil HY (2016) A fingerprint spoofing detection system using LBP. In: 2016 International conference on electrical, electronics, and optimization techniques (ICEEOT). IEEE, pp 3413–3419

  13. Ghiani L, Marcialis GL, Roli F (2012) Fingerprint liveness detection by local phase quantization. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012). IEEE, pp 537–540

  14. Kim W, Suh S, Han J-J (2015) Face liveness detection from a single image via diffusion speed model. IEEE Trans Image Process 24(8):2456–2465

    Article  ADS  MathSciNet  Google Scholar 

  15. Kim W (2016) Fingerprint liveness detection using local coherence patterns. IEEE Signal Process Lett 24(1):51–55

    Article  ADS  MathSciNet  Google Scholar 

  16. Chaudhari A, Deore P (2012) Prevention of spoof attacks in fingerprinting using histogram features. In: 2012 Nirma University international conference on engineering (NUiCONE). IEEE, pp 1–4

  17. Ghiani L, Hadid A, Marcialis GL, Roli F (2016) Fingerprint liveness detection using local texture features. IET Biom 6(3):224–231

    Article  Google Scholar 

  18. Shaju S, Davis D (2017) Haar wavelet transform based histogram concatenation model for finger print spoofing detection. In: 2017 International conference on communication and signal processing (ICCSP). IEEE, pp 1352–1356

  19. Yuan C, Sun X (2018) Fingerprint liveness detection using histogram of oriented gradient based texture feature. J Internet Technol 19(5):1499–1507

    Google Scholar 

  20. Zaghetto C, Mendelson M, Zaghetto A, Vidal FdB (2017) Liveness detection on touchless fingerprint devices using texture descriptors and artificial neural networks. In: 2017 IEEE international joint conference on biometrics (IJCB). IEEE, pp 406–412

  21. Liu Z, Cao H, Zhang H, Lai J (2020) A fingerprint image enhancement method based on anisotropic diffusion and shock filtering. In: 2020 2nd international conference on information technology and computer application (ITCA). IEEE, pp 401–404

  22. Sabeena M, Abraham L, Varghese A (2021) Digital image forgery detection using local binary pattern (LBP) and Harlick transform with classification. In: 2021 IEEE international power and renewable energy conference (IPRECON). IEEE, pp 1–6

  23. Chen M, Yuan C, Li X, Zhou Z (2022) Broad learning with uniform local binary pattern for fingerprint liveness detection. In: Neural computing for advanced applications: third international conference, NCAA 2022, Jinan, China, July 8–10, 2022, Proceedings, Part I. Springer, pp 327–340

  24. Sharma D, Selwal A (2022) An intelligent approach for fingerprint presentation attack detection using ensemble learning with improved local image features. Multimedia Tools Appl 81(16):22 129-22 161

    Article  Google Scholar 

  25. Agrawal R, Jalal AS, Arya KV (2019) Fake fingerprint liveness detection based on micro and macro features. Int J Biom 11(2):177–206

    Google Scholar 

  26. Ogunlana OS (2023) Impact of noisy singular point detection on performance of fingerprint matching. Covenant J Inf Commun Technol

  27. Feng Y, Kumar A (2023) Detecting locally, patching globally: an end-to-end framework for high speed and accurate detection of fingerprint minutiae. IEEE Trans Inf Forensic Secur 18:1720–1733

    Article  Google Scholar 

  28. Guillaro F, Cozzolino D, Sud A, Dufour N, Verdoliva L (2023) TruFor: leveraging all-round clues for trustworthy image forgery detection and localization. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition. pp 20 606–20 615

  29. Hamadouche M, Khalil Z, Tebbi H, Guerroumi M, Zafoune Y (2023) A replay attack detection scheme based on perceptual image hashing. Multimedia Tools Appl 1–33

  30. Baskar M, Renuka Devi R, Ramkumar J, Kalyanasundaram P, Suchithra M, Amutha B (2023) Region centric minutiae propagation measure orient forgery detection with finger print analysis in health care systems. Neural Process Lett 55(1):19–31

    Article  Google Scholar 

  31. Hameed SS, Ahmed IT, Al Okashi OM (2023) Real and altered fingerprint classification based on various features and classifiers. Comput Mater Continua 74(1)

  32. Singh P, Shankar A (2021) A novel optical image denoising technique using convolutional neural network and anisotropic diffusion for real-time surveillance applications. J Real-Time Image Process 1–18

  33. Olisa O, Iloanusi O, Chijindu V, Ahaneku M (2018) Edge detection in images using Haar wavelets, Sobel, Gabor an d Laplacian filters. Int J Sci Res 7:46–49

    Google Scholar 

  34. Thirumaleshwari Devi B, Shitharth S (2021) Multiple face detection using Haar-AdaBoosting, Lbp-AdaBoosting and neural networks. In: IOP conference series: materials science and engineering, vol 1042. pp 012017

  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

    Article  Google Scholar 

  36. Galbally J, Fierrez J, Alonso-Fernandez F, Martinez-Diaz M (2011) Evaluation of direct attacks to fingerprint verification systems. Telecommun Syst 47(3–4):243–254

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Wireless Communication and Signal Processing (WCSP), Faculty of Electrical Engineering, Sahand University of Technology, Sahand, Iran

    Zahra Farzadpour & Masoumeh Azghani

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  1. Zahra Farzadpour
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  2. Masoumeh Azghani
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Correspondence to Masoumeh Azghani.

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Farzadpour, Z., Azghani, M. Adaptive thresholding pattern for fingerprint forgery detection. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18649-3

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