Skip to main content
SpringerLink
Log in

Directional filter bank-based fingerprint image quality

  • Theoretical advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Fingerprint image quality ensures that only the high-quality fingerprints containing a good amount of features are used for verification. Fingerprint matching accuracy depends heavily on the quality of the fingerprints. In this paper, we have proposed a new fingerprint image quality method based on the Directional Filter Banks (DFB). The fingerprint image is decomposed into subbands using the DFB. The similarity between the different subbands is used to calculate the fingerprint image quality. We have compared the performance of the proposed method with the ten existing fingerprint quality estimation methods with special emphasis on widely used fingerprint quality metrics NFIQ and NFIQ 2.0. The experimental results on the top three high-quality fingerprints in the FVC 2004 DB2A dataset show an equal error rate (EER) of 5.59% for the proposed method as compared with 5.74% and 7.95% for NFIQ 2.0 and NFIQ, respectively. Ranked EER, ROC, and error-reject curves show that DFB-based method is a good predictor of matching performance, and it outperforms the existing fingerprint quality methods. We have also analyzed the effect of the partial fingerprints on the fingerprint recognition system using the FVC 2004 DB1A dataset. The presence of partial fingerprints has an adverse effect on the recognition system. The equal error rate increases from 2.34 to 13.93% in partial fingerprint recognition. The proposed DFB-based method rightly assigns low-quality values to partial fingerprints.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Jain AK, Ross AA, Nandakumar K (2011) Introduction to biometrics. Springer, Berlin

    Book  Google Scholar 

  2. Prabakhar S, Jain AK, Maio D, Maltoni D (2003) Handbook of fingerprint recognition. Springer, Berlin

    MATH  Google Scholar 

  3. Aadhar uidai. https://uidai.gov.in/. Accessed: 30 Jan 2019

  4. Li SZ, Jain A (2015) Encyclopedia of biometrics. Springer, Berlin

    Book  Google Scholar 

  5. Fierrez-Aguilar J, Chen Y, Ortega-Garcia J, Jain AK (2006) Incorporating image quality in multi-algorithm fingerprint verification. In: ICB. Springer, pp 213–220

  6. Liu X, Pedersen M, Charrier C, Bours P, Busch C (2016) The influence of fingerprint image degradations on the performance of biometric system and quality assessment. In: 2016 international conference of the Biometrics Special Interest Group (BIOSIG). IEEE, pp 1–6

  7. Simon-Zorita D, Ortega-Garcia J, Fierrez-Aguilar J, Gonzalez-Rodriguez J (2003) Image quality and position variability assessment in minutiae-based fingerprint verification. IEE Proc-Vis Image Signal Process 150(6):402–408

    Article  Google Scholar 

  8. Yang R (2018) Effects of sensors, age, and gender on fingerprint image quality. PhD thesis, Carleton University

  9. Alsmirat MA, Al-Alem F, Al-Ayyoub M, Jararweh Y, Gupta B (2019) Impact of digital fingerprint image quality on the fingerprint recognition accuracy. Multimedia Tools Appl 78(3):3649–3688

    Article  Google Scholar 

  10. Hendre M, Patil S, Abhyankar A (2021) Utility of quality metrics in partial fingerprint recognition. Int J Comput Digit Syst 10:839–849

    Article  Google Scholar 

  11. Nandakumar K, Chen Y, Jain AK, Dass SC (2006) Quality-based score level fusion in multibiometric systems. In: 18th international conference on pattern recognition (ICPR’06), vol 4. IEEE, pp 473–476

  12. Jain AK, Feng J (2010) Latent fingerprint matching. IEEE Trans Pattern Anal Mach Intell 33(1):88–100

    Article  Google Scholar 

  13. Jain AK, Prabhakar S, Hong L, Pankanti S (2000) Filterbank-based fingerprint matching. IEEE Trans Image Process 9(5):846–859

    Article  Google Scholar 

  14. Senior AW, Bolle RM (2001) Improved fingerprint matching by distortion removal. IEICE Trans Inf Syst 84(7):825–832

    Google Scholar 

  15. Chikkerur S, Govindaraju V, Cartwright AN (2005) Fingerprint image enhancement using STFT analysis. In: International conference on pattern recognition and image analysis. Springer, pp 20–29

  16. Greenberg S, Aladjem M, Kogan D (2002) Fingerprint image enhancement using filtering techniques. Real-Time Imaging 8(3):227–236

    Article  Google Scholar 

  17. Gupta R, Khari M, Gupta D, Crespo RG (2020) Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Inf Sci 530:201–218

    Article  MathSciNet  Google Scholar 

  18. Han K, Wang Z, Chen Z (2018) Fingerprint image enhancement method based on adaptive median filter. In: 2018 24th Asia–Pacific conference on communications (APCC), pp 40–44. IEEE

  19. He Y, Tian J, Luo X, Zhang T (2003) Image enhancement and minutiae matching in fingerprint verification. Pattern Recognit Lett 24(9–10):1349–1360

    Article  Google Scholar 

  20. 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 

  21. Khan MA (2011) Fingerprint image enhancement and minutiae extraction

  22. ISO/IEC 29794-1:2016 Information technology—biometric sample quality—part 1: framework. https://www.iso.org/standard/62782.html. Accessed: 9 Aug 2018

  23. Bamberger RH, Smith MJ (1992) A filter bank for the directional decomposition of images: theory and design. IEEE Trans Signal Process 40(4):882–893

    Article  Google Scholar 

  24. Chan W, Law N, Siu W (2003) Multiscale feature analysis using directional filter bank. In: Fourth international conference on information, communications and signal processing, 2003 and the fourth Pacific Rim conference on multimedia. Proceedings of the 2003 Joint, vol 2. IEEE, pp 822–826

  25. Grother P, Tabassi E (2007) Performance of biometric quality measures. IEEE Trans Pattern Anal Mach Intell 29(4):531–543

    Article  Google Scholar 

  26. Alonso-Fernandez F, Fierrez J, Ortega-Garcia J, Gonzalez-Rodriguez J, Fronthaler H, Kollreider K, Bigun J (2007) A comparative study of fingerprint image-quality estimation methods. IEEE Trans Inf Forensics Secur 2(4):734–743

    Article  Google Scholar 

  27. Olsen MA, Šmida V, Busch C (2016) Finger image quality assessment features-definitions and evaluation. IET Biom 5(2):47–64

    Article  Google Scholar 

  28. Yao Z, Le Bars JM, Charrier C, Rosenberger C (2018) Comparative study of digital fingerprint quality assessment metrics. In: 2018 international conference on biometrics (ICB). IEEE

  29. Chen TP, Jiang X, Yau WY (2004) Fingerprint image quality analysis. In: 2004 international conference on image processing, 2004. ICIP’04, vol 2. IEEE, pp 1253–1256

  30. Lim E, Jiang X, Yau W (2002) Fingerprint quality and validity analysis. In: 2002 international conference on image processing. 2002. Proceedings, vol 1. IEEE, pp I–I

  31. Lim E, Toh KA, Suganthan P, Jiang X, Yau WY (2004) Fingerprint image quality analysis. In: 2004 international conference on image processing, 2004. ICIP’04, vol 2. IEEE, pp 1241–1244

  32. Chen Y, Dass SC, Jain AK (2005) Fingerprint quality indices for predicting authentication performance. In: International conference on audio-and video-based biometric person authentication. Springer, pp 160–170

  33. Nanni L, Lumini A (2007) A hybrid wavelet-based fingerprint matcher. Pattern Recognit 40(11):3146–3151

    Article  Google Scholar 

  34. Tabassi E, Wilson C, Watson C (2004) Fingerprint image quality. nistir7151. https://doi.org/10.6028/NIST.IR.7151

    Article  Google Scholar 

  35. Olsen M, Busch C (2011) Deficiencies in NIST fingerprint image quality algorithm. 12. Deutscher IT-Sicherheitskongress 1:251–262

    Google Scholar 

  36. Merkle J, Schwaiger M, Bausinger O, Breitenstein M, Elwart K, Nuppeney M (2010) Towards improving the NIST fingerprint image quality (NFIQ) algorithm (extended version). arXiv preprint arXiv:1008.0781

  37. Khurjekar I, Garware B, Abhyankar A (2015) Towards minimizing effect of partial fingerprint images on the performance of fingerprint recognition systems. In: 2015 international conference on information processing (ICIP). IEEE, pp 839–842

  38. Development of NFIQ 2.0. https://www.nist.gov/services-resources/software/development-nfiq-20. Accessed: 10 Sept 2018

  39. Priesnitz J, Rathgeb C, Buchmann N, Busch C (2020) Touchless fingerprint sample quality: Prerequisites for the applicability of NFIQ2. 0. In: 2020 international conference of the Biometrics Special Interest Group (BIOSIG). IEEE, pp 1–5

  40. Aastrup Olsen M, Tabassi E, Makarov A, Busch C (2013) Self-organizing maps for fingerprint image quality assessment. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 138–145

  41. Yan J, Dai X, Zhao Q, Liu F (2017) A CNN-based fingerprint image quality assessment method. In: Chinese conference on biometric recognition. Springer, pp 344–352

  42. Sharma RP, Dey S (2019) Fingerprint image quality assessment and scoring using minutiae centered local patches. J Electron Imaging 28(1):013016

    Article  Google Scholar 

  43. Roy A, Memon N, Ross A (2017) Masterprint: exploring the vulnerability of partial fingerprint-based authentication systems. IEEE Trans Inf Forensics Secur 12(9):2013–2025

    Article  Google Scholar 

  44. Bontrager P, Roy A, Togelius J, Memon N, Ross A (2018) Deepmasterprints: generating masterprints for dictionary attacks via latent variable evolution. In: 2018 IEEE 9th International conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, pp 1–9

  45. Joshi M, Mazumdar B, Dey S (2019) A novel approach for partial fingerprint identification to mitigate masterprint generation. arXiv preprint arXiv:1911.03052

  46. Shen L, Kot A, Koo W (2001) Quality measures of fingerprint images. In: International conference on audio-and video-based biometric person authentication. Springer, pp 266–271

  47. Olsen MA, Xu H, Busch C (2012) Gabor filters as candidate quality measure for NFIQ 2.0. In: 2012 5th IAPR international conference on biometrics (ICB). IEEE, pp 158–163

  48. Minh N (2002) Directional multiresolution image representations. PhD thesis, Citeseer

  49. Li H, Liu L, Huang W, Yue C (2016) An improved fusion algorithm for infrared and visible images based on multi-scale transform. Infrared Phys Technol 74:28–37

    Article  Google Scholar 

  50. Ma J, Ma Y, Li C (2019) Infrared and visible image fusion methods and applications: a survey. Inf Fusion 45:153–178

    Article  Google Scholar 

  51. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106

    Article  Google Scholar 

  52. Khan MA, Khan TM (2013) Fingerprint image enhancement using data driven directional filter bank. Optik 124(23):6063–6068

    Article  Google Scholar 

  53. Oh SK, Lee JJ, Park CH, Kim BS, Park KH (2003) New fingerprint image enhancement using directional filter bank

  54. Park Si, Smith MJ, Lee JJ (2000) Fingerprint enhancement based on the directional filter bank. In: Proceedings 2000 international conference on image processing (Cat. No. 00CH37101), vol 3. IEEE, pp 793–796

  55. Park CH, Lee JJ, Smith MJ, Si Park, Park KH (2004) Directional filter bank-based fingerprint feature extraction and matching. IEEE Trans Circuits Syst Video Technol 14(1):74–85

    Article  Google Scholar 

  56. Li C, Fu B, Li J, Yang X (2012) Texture-based fingerprint recognition combining directional filter banks and wavelet. Int J Pattern Recognit Artif Intell 26(04):1256012

    Article  MathSciNet  Google Scholar 

  57. Fvc2004 dataset. http://bias.csr.unibo.it/fvc2004/databases.asp. Accessed: 15 Aug 2018

  58. Cappelli R, Maio D, Maltoni D, Wayman JL, Jain AK (2005) Performance evaluation of fingerprint verification systems. IEEE Trans Pattern Anal Mach Intell 28(1):3–18

    Article  Google Scholar 

  59. Ko K (2007) Users guide to export controlled distribution of NIST biometric image software (NBIS-EC). Technical report

  60. NIST biometric image software (NBIS). https://www.nist.gov/services-resources/software/nist-biometric-image-software-nbis. Accessed: 5 Aug 2018

  61. Aravindan A, Anzar S (2017) Robust partial fingerprint recognition using wavelet sift descriptors. Pattern Anal Appl 20(4):963–979

    Article  MathSciNet  Google Scholar 

  62. Malathi S, Meena C (2010) An efficient method for partial fingerprint recognition based on local binary pattern. In: 2010 international conference on communication control and computing technologies. IEEE, pp 569–572

  63. Chen F, Zhou J (2012) On the influence of fingerprint area in partial fingerprint recognition. In: Chinese conference on biometric recognition. Springer, pp 104–111

Download references

Author information

Authors and Affiliations

  1. Department of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India

    Manik Hendre, Suraj Patil & Aditya Abhyankar

Authors
  1. Manik Hendre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suraj Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aditya Abhyankar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manik Hendre.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hendre, M., Patil, S. & Abhyankar, A. Directional filter bank-based fingerprint image quality. Pattern Anal Applic 25, 379–393 (2022). https://doi.org/10.1007/s10044-021-01042-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-021-01042-3

Keywords

Search

Navigation