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A Study of Friction Ridge Distortion Effect on Automated Fingerprint Identification System – Database Evaluation

  • Łukasz Hamera
  • Łukasz WięcławEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11127)

Abstract

Fingerprint identification is an important part of forensic science (e.g. criminal investigations or identity verification). Friction ridge impressions left at the crime scene can be affected by the nonlinear distortion due to elasticity of the skin, pressure changes or finger movement during deposition. These deformations affect relative distances between fingerprint features such as minutiae point, ridge frequency and orientation, which eventually leads to difficulties in establishing a positive match between impressions of the same finger.

In this study we present preliminary results of the impact of fingerprint friction ridge distortion on NBIS Bozorth3 fingerprint matching algorithm. For this purpose special fingerprint database was developed. The database contained 5175 prints obtained from 40 volunteers. Experimental results reveal that the some types of fingerprint distortion (especially movement to right and left) impacts the recognition performance. The results of our studies can be used in future work on statistical friction ridge analysis and fingerprint algorithms robust to distortions.

Keywords

Fingerprint Friction ridge Deformation Distortion Database AFIS NBIS Biometrics 

References

  1. 1.
    Porwik, P.: The modern techniques of latent fingerprint imaging. In: 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), Krackow, pp. 29–33 (2010)Google Scholar
  2. 2.
    Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)CrossRefGoogle Scholar
  3. 3.
    Doroz, R., Wrobel, K., Porwik, P.: An accurate fingerprint reference point determination method based on curvature estimation of separated ridges. Int. J. Appl. Math. Comput. Sci. 28(1), 209–225 (2018)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Surmacz, K., Saeed, K., Rapta, P.: An improved algorithm for feature extraction from a fingerprint fuzzy image. Opt. Appl. 43(3), 515–527 (2013)Google Scholar
  5. 5.
    Maio, D., Maltoni, D.: Direct gray-scale minutiae detection in fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 19(1), 27–40 (1997)CrossRefGoogle Scholar
  6. 6.
    Chen, X., Tian, J., Yang, X., Zhang, Y.: An algorithm for distorted fingerprint matching based on local triangle feature set. IEEE Trans. Inf. Forensics Secur. 1, 169–177 (2006)CrossRefGoogle Scholar
  7. 7.
    Bazen, A., Gerez, S.: Fingerprint matching by thin-plate spline modelling of elastic deformations. Pattern Recogn. 36, 1859–1867 (2003)CrossRefGoogle Scholar
  8. 8.
    Tabassi, E., Wilson, C., Watson, C.: Fingerprint Image Quality. NISTIR 7151 (2004)Google Scholar
  9. 9.
    Dvornychenko, V.N., Garris, M.D.: Summary of NIST Latent Fingerprint Testing Workshop. NISTIR 7377 (2006)Google Scholar
  10. 10.
    Si, X., Feng, J., Zhou, J.: Detecting fingerprint distortion from a single image. In: Proceedings IEEE International Workshop Information Forensics Security, pp. 1–6 (2012)Google Scholar
  11. 11.
    Ratha, N.K., Karu, K., Chen, S., Jain, A.K.: A real-time matching system for large fingerprint databases. IEEE TPAMI 18(8), 799–813 (1996)CrossRefGoogle Scholar
  12. 12.
    Kovacs-Vajna, Z.M.: A fingerprint verification system based on triangular matching and dynamic time warping. IEEE TPAMI 22(11), 1266–1276 (2000)CrossRefGoogle Scholar
  13. 13.
    Ross, A., Shah, S., Shah, J.: Image versus feature mosaicking: a case study in fingerprints. In: Proceedings SPIE, pp. 620208-1– 620208-12 (2006)Google Scholar
  14. 14.
    Ross, A., Dass, S., Jain, A.K.: A deformable model for fingerprint matching. Pattern Recogn. 38, 95–103 (2005)CrossRefGoogle Scholar
  15. 15.
    Ross, A., Dass, S., Jain, A.K.: Fingerprint warping using ridge curve correspondences. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 19–30 (2006)CrossRefGoogle Scholar
  16. 16.
    Cao, K., Yang, X., Tao, X., Li, P., Zang, Y., Tian, J.: Combining features for distorted fingerprint matching. J. Netw. Comput. Appl. 33, 258–267 (2010)CrossRefGoogle Scholar
  17. 17.
    Chen, Y., Dass, D., Ross, A., Jain, A.K.: Fingerprint deformation models using minutiae locations and orientations. In: Proceedings IEEE Workshop on Applications of Computer Vision, pp. 150–155 (2005)Google Scholar
  18. 18.
    Cappelli, R., Maio, D., Maltoni, D.: Modelling plastic distortion in fingerprint images. In: Singh, S., Murshed, N., Kropatsch, W. (eds.) ICAPR 2001. LNCS, vol. 2013, pp. 371–378. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-44732-6_38CrossRefGoogle Scholar
  19. 19.
    Uz, T., Bebis, G., Erol, A., Prabhakar, S.: Minutiae-based template synthesis and matching for fingerprint authentication. Comput. Vis. Image Underst., 979–992 (2009)Google Scholar
  20. 20.
    Singh, R., Vatsa, M., Noore, A.: Improving verification accuracy by synthesis of locally enhanced biometric images and deformable model. Sig. Process. 87, 2746–2764 (2007)CrossRefGoogle Scholar
  21. 21.
    Watson, C., Grother, P., Cassasent, D.: Distortion-tolerant filter for elastic-distorted fingerprint matching. In: Proceedings SPIE Optical Pattern Recognition, pp. 166–174 (2000)Google Scholar
  22. 22.
    Senior, A., Bolle, R.: Improved fingerprint matching by distortion removal. IEICE Trans. Inf. Syst. 84(7), 825–831 (2001)Google Scholar
  23. 23.
    Dabouei, A., Kazemi, H., Iranmanesh, S.M., Dawson, J., Nasrabadi, N.M.: Fingerprint distortion rectification using deep convolutional neural networks. In: The 11th IAPR International Conference on Biometrics, CoRR abs/1801.01198 (2018)Google Scholar
  24. 24.
    Watson, C.I.: NIST Special Database 24 Digital Video of Live-Scan Fingerprint Data, U.S. National Institute of Standards and Technology (1998)Google Scholar
  25. 25.
    Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2000: fingerprint verification competition. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 402–412 (2002)CrossRefGoogle Scholar
  26. 26.
    Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2002: second fingerprint verification competition. In: Object Recognition Supported by User Interaction for Service Robots, vol. 3, pp. 811–814 (2002)Google Scholar
  27. 27.
    Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2004: third fingerprint verification competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 1–7. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-25948-0_1CrossRefGoogle Scholar
  28. 28.
    Gao, Q., Zhang, X.: A study of distortion effects on fingerprint matching. Comput. Sci. Eng. 2(3), 37–42 (2012)CrossRefGoogle Scholar
  29. 29.
    Si, X., Feng, J., Zhou, J., Luo, Y.: Detection and rectification of distorted fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 555–568 (2015)CrossRefGoogle Scholar
  30. 30.
    Ko, K., Salamon, W.J.: NIST Biometric Image Software (NBIS) https://www.nist.gov/services-resources/software/nist-biometric-image-software-nbis. Accessed 29 Mar 2018
  31. 31.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of Bielsko-BialaBielsko-BialaPoland

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