Skip to main content
Log in

Spoofing free fingerprint image enhancement

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Fingerprint spoofing is a method of bypassing the security of a biometric fingerprint system by using artificial fingerprints created with various materials and techniques. Numerous enhancement technologies are available to improve the quality of the spoof-free fingerprint image. Existing technology requires more processing time for terrible ridge and valley; significant security challenges for authenticating a legal person. The paper proposes a novel Fingerprint Enhancement, object area detection, and Gabor-based ridge era (FEOG) model to enhance the spoof-free fingerprint image. The new method includes morphological finger item vicinity reputation, a normalization process, and Gabor-based ridge enhancement for higher enhancement for better authentication. Our proposed method outperforms the SH-DB-MOL database when tested with various metrics.

.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Khan TM, Khan MAU, Kong Y (2014) Fingerprint image enhancement using multi-scale DDFB based diffusion filters and modified Hong filters. Elsevier, Amsterdam, pp 4206–4214

    Google Scholar 

  2. Yun E, Cho S (2006) Adaptive fingerprint image enhancement with fingerprint image quality analysis. Image Vis Comput 24(1):101–110

    Article  Google Scholar 

  3. Ogbuokiri BO, Agu M (2015) An enhanced authentication system using face and fingerprint technologies. IOSR J Comput Eng 17(6):74–84

    Google Scholar 

  4. Senthil Selvi A, Kumar KPM, Dhanasekeran S, Maheswari PU, Ramesh S, Pandi SS (2020) De-noising of images from salt and pepper noise using hybrid filter, fuzzy logic noise detector and genetic optimization algorithm (HFGOA). Multimed Tools Appl 79(5–6):4115–4131

    Article  Google Scholar 

  5. Qi Y et al (2022) A comprehensive overview of image enhancement techniques. Arch Comput Methods Eng 29(1):583–607. https://doi.org/10.1007/s11831-021-09587-6

    Article  MathSciNet  Google Scholar 

  6. Gupta R, Khari M, Gupta D, Crespo RG (2020) Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Information science (New York). Elsevier, Amsterdam, pp 201–218

    Google Scholar 

  7. Ali SF, Khan MA, Aslam AS (2021) Fingerprint matching, spoof and liveness detection: classification and literature review. Front Comput Sci. https://doi.org/10.1007/s11704-020-9236-4

    Article  Google Scholar 

  8. Hong L, Wan Y, Jain A (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789

    Article  Google Scholar 

  9. Deshpande UU, Malemath VS, Patil SM, Chaugule SV (2022) Automatic latent fingerprint identification system using scale and rotation invariant minutiae features. Int J Inf Technol 14(2):1025–1039. https://doi.org/10.1007/s41870-020-00508-7

    Article  Google Scholar 

  10. Lee CS, Kuo YH, Yu PT (1997) Weighted fuzzy mean filters for image processing. Fuzzy Sets Syst 89(2):157–180

    Article  Google Scholar 

  11. Pankanti S, Prabhakar S, Jain AK (2002) On the individuality of fingerprints. IEEE Trans Pattern Anal Mach Intell 24(8):1010–1025

    Article  MATH  Google Scholar 

  12. Farbiz F, Menhaj MB, Motamedi SA, Hagan MT (2000) A new fuzzy logic filter for image enhancement. IEEE Trans Syst Man Cybern Part B Cybern 30(1):110–119

    Article  Google Scholar 

  13. Liu Q, He Y (2020) Robust Geman-McClure Based nonlinear spline adaptive filter against impulsive noise. IEEE Access 8:22571–22580. https://doi.org/10.1109/ACCESS.2020.2969219

    Article  Google Scholar 

  14. Wang S, Wang Y (2004) Fingerprint enhancement in the singular point area. IEEE Signal Process Lett 11(1):16–19

    Article  Google Scholar 

  15. Hasan H, Abdul-Kareem S (2013) Fingerprint image enhancement and recognition algorithms: a survey. Neural Comput Appl 23(6):1605–1610

    Article  Google Scholar 

  16. Clausi DA, Deng H (2005) Design-based texture feature fusion using Gabor filters and co-occurrence probabilities. IEEE Trans Image Process 14(17):925–936

    Article  Google Scholar 

  17. Chen C, Chen DC (1996) Multi-resolution Gabor filter in texture analysis. Pattern Recognit Lett 17(10):1069–1076

    Article  Google Scholar 

  18. Mehrotra R, Namuduri KR, Ranganathan N (1992) Gabor filter-based edge detection. Pattern Recognit 25(12):1479–1494

    Article  Google Scholar 

  19. Kumar M, Priyanka (2019) Various image enhancement and matching techniques used for fingerprint recognition system. Int J Inf Technol 11(4):767–772. https://doi.org/10.1007/s41870-017-0061-4

    Article  Google Scholar 

  20. Farooq H, Naaz S (2020) Performance analysis of biometric recognition system based on palmprint. Int J Inf Technol 12(4):1281–1289. https://doi.org/10.1007/s41870-018-0230-0

    Article  Google Scholar 

  21. Fu B, Zhao X, Song C, Li X, Wang X (2019) A salt and pepper noise image denoising method based on the generative classification. Multimed Tools Appl 78(9):12043–12053

    Article  Google Scholar 

  22. Gangonda SS, Patavardhan PP, Karande KJ (2022) VGHN: variations aware geometric moments and histogram features normalization for robust uncontrolled face recognition. Int J Inf Technol 14(4):1823–1834. https://doi.org/10.1007/s41870-021-00703-0

    Article  Google Scholar 

  23. Khan TM, Bailey DG, Mohammad A, Khan U, Kong Y (2017) Efficient hardware implementation for fingerprint image enhancement using anisotropic Gaussian filter. IEEE Trans Image Process 14(8):2116–2126

    Article  MathSciNet  MATH  Google Scholar 

  24. Yang J, Liu L, Jiang T, Fan Y (2003) A modified Gabor filter design method for fingerprint image enhancement. Pattern Recogn Lett 24(12):1805–1817

    Article  Google Scholar 

  25. Arun R, Nair MS, Vrinthavani R, Tatavarti R (2011) An alpha rooting based hybrid technique for image enhancement. Eng Lett 19(3):159–168

    Google Scholar 

  26. Sankaran A, Vatsa M, Singh R (2015) Multisensor optical and latent fingerprint database. IEEE Access 3:653–665. https://doi.org/10.1109/ACCESS.2015.2428631

    Article  Google Scholar 

  27. Jain AK, Feng J (2011) Latent fingerprint matching. IEEE Trans 33(1):88–100

    Google Scholar 

  28. Pilevar AH, Saien S, Khandel M, Mansoori B (2015) A new filter to remove salt and pepper noise in color images. Signal Image Video Process 9(4):779–786

    Article  Google Scholar 

  29. Bai T, Tan J (2015) Automatic detection and removal of high-density impulse noises. IET Image Process 9(2):162–172

    Article  Google Scholar 

  30. Lin PH, Chen BH, Cheng FC, Huang SC (2016) A morphological mean filter for impulse noise removal. J Disp Technol 12(4):344–350

    Google Scholar 

  31. Bhadouria VS, Ghoshal D (2016) A study on genetic expression programming-based approach for impulse noise reduction in images. Signal Image Video Process 10(3):575–584

    Article  Google Scholar 

  32. Faragallah OS, Ibrahem HM (2016) Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-Int J Electron Commun 70(8):1034–1040

    Article  Google Scholar 

  33. Arora S, Bhatia MPS (2020) Fingerprint spoofing detection to improve customer security in mobilefinancial applications using deep learning. Arab J Sci Eng 45(4):2847–2863

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Fei J, Xia Z, Yu P, Xiao F (2020) Adversarial attacks on fingerprint liveness detection. Eurasip J Image Video Process 2020(1):1–11

    Article  Google Scholar 

  36. Chen J, Zhan Y, Cao H, Wu X (2018) Adaptive probability filter for removing salt and pepper noise. IET Image Proc 12(6):863–871

    Article  Google Scholar 

  37. Erkan U, Thanh DNH, Hieu LM, Enginoglu S (2019) An iterative mean filter for image denoising. IEEE Access 7:167847–167859

    Article  Google Scholar 

Download references

Acknowledgements

I would like to acknowledge and express my heartfelt gratitude to my supervisor who assisted me in writing this research paper with valuable ideas. His guidance and advice were extremely helpful in completing this research paper at all levels. I'd also like to express my heartfelt gratitude to my entire family for their unending assistance and knowledge when assigning my studies and to write my paper. Your prayer for me has been what has kept me going this far. As a result, I’d like to thank God for allowing me to go through all of this. Every day, I've improved my steering. I can continue to put my faith in you for my future.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H. Mohamed Khan.

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

Khan, H.M., Venkadesh, P. Spoofing free fingerprint image enhancement. Int. j. inf. tecnol. 15, 477–485 (2023). https://doi.org/10.1007/s41870-022-01129-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-022-01129-y

Keywords

Navigation