A Comparative Study of Various Minutiae Extraction Methods for Fingerprint Recognition Based on Score Level Fusion

  • P. Aruna KumariEmail author
  • G. JayaSuma
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


A Multimodal Biometric system combines the evidences from various biometric sources or multiple evidences from single biometric source to atone for the limitations in performance of unimodal biometric system. This paper discusses two Minutiae extraction techniques to recognize fingerprint based on confidence level fusion of two extracted features, bifurcations and ridge endings and compares the recognition accuracy. In particular, the well known Morphological based minutiae extraction approach is compared with the proposed fuzzy logic control based approach. Experimental results based on IITD fingerprint database demonstrate that the score level fusion of bifurcations and ridge endings for fingerprint leads to a dramatically improvement in performance. And also the results reveal that our proposed fuzzy logic control based minutiae extraction is much more reliable than the Morphological based minutiae extraction approach.


Multimodal biometrics Fingerprint Fuzzy logic control Morphology Score level fusion ROC 


  1. 1.
    Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circ Syst Video Technol 14(1):4–20 (special issue on image- and video-based biometric)Google Scholar
  2. 2.
    Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Hand book of fingerprint recognition. Springer, BerlinGoogle Scholar
  3. 3.
    Cui FF, Yang GP (2011) Score level fusion of fingerprint and finger vein recognition. J Comput Inf Syst 7:5723–5731Google Scholar
  4. 4.
    Ross AA, Nandakumar K, Jain AK (2006) Handbook of multibiometrics. Springer, BerlinGoogle Scholar
  5. 5.
    Jain A et al (2005) Score Normalization in multimodal biometric systems. Pattern Recognit 38:2270–2285CrossRefGoogle Scholar
  6. 6.
    Ross A, Jain AK (2003) Information fusion in biometrics. Pattern Recognit Lett 24(13):2115–2125 (special issue on multimodal biometrics)Google Scholar
  7. 7.
    Lip CC, Ramli DA (2012) Comparative study on feature, score and decision level fusion schemes for robust multibiometric systems. In: Sambath S, Zhu E (eds) Frontiers in computer education, AISC 133. Springer, Berlin, pp 941–948Google Scholar
  8. 8.
    Jain AK, Ross AA, Nandakumar K (2011) Introduction to biometrics. Springer, BerlinGoogle Scholar
  9. 9.
    Lu H, Jiang X, Yan W-Y (2002) Effective and efficient fingerprint image post processing, vol 2Google Scholar
  10. 10.
    Ratha NK, Chen S, Jain AK (1995) Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognit 28(11):1657–1672CrossRefGoogle Scholar
  11. 11.
    Mehtre BM (1993) Fingerprint image analysis for automatic identification. Mach Vision Appl 6:124–139CrossRefGoogle Scholar
  12. 12.
    Farina A, Kovács-Vajna ZM, Leone A (1999) Fingerprint minutiae extraction from skeletonized binary images. Pattern Recognit 32(5):877–889CrossRefGoogle Scholar
  13. 13.
    Sagar VK, Ngo DBL, Foo KCK (1995) Fuzzy feature selection for fingerprint identification. In: IEEE 29th annual 1995 international Carnahan conference on security technology, Sanderstead, 18–20 Oct 1995Google Scholar
  14. 14.
    Deutsch ES (1972) Thinning algorithm on rectangular, hexagonal and triangular arrays. Commun ACM 15(9):827–837Google Scholar
  15. 15.
    Sagar VK, Berstecher RG (1994) Fuzzy control for feature extraction from fingerprint images. In: Second European congress on intelligent techniques and soft computing (EUFIT94), Aachen, Germany, 20–23 Sept 1994Google Scholar
  16. 16.
    O’Gorman L, Nickerson JV (1989) An approach to fingerprint filter design. Pattern Recognit 22(1):29–38CrossRefGoogle Scholar
  17. 17.
    Xiao Q, Raafat H (1991) Fingerprint image postprocessing: a combined statistical and structural approach. Pattern Recognit 24(10):985–992CrossRefGoogle Scholar
  18. 18.
    Zhao F, Tang X (2007) Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction. Pattern Recognit 40:1270–1281CrossRefzbMATHGoogle Scholar
  19. 19.
    Kumar A et al (2013) Fuzzy binary decision tree for biometric based personal authentication. Neuro Comput 99:87–97Google Scholar
  20. 20.
    Hasan H, Abdul-Kareem S (2013) Fingerprint image enhancement and recognition algorithms: a survey. Neural Comput Appl 23:1605–1610Google Scholar
  21. 21.
    Kamei T, Mizoguchi M (1995) Image filter design for fingerprint enhancement. In: Proceedings of the international symposium on computer vision, pp 109–114Google Scholar
  22. 22.
    Hsieh CT, Lai E, Wang YC (2003) An effective algorithm for fingerprint image enhancement based on wavelet transform. Pattern Recognit 36(2):303–312CrossRefGoogle Scholar
  23. 23.
    Bansal R, Sehagal P, Bedi P (2010) Effective morphological extraction of true fingerprint minutiae based on the hit or miss transform. Int J Biometrics Bioinf, 4(2):71–85Google Scholar
  24. 24.
    Espinosa V (2002) Mathematical morphological approaches for fingerprint thinning. IEEEGoogle Scholar
  25. 25.
    Rutovitz D (1966) Pattern recognition. J Roy Stat Soc 129:504–530CrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJNTUK-UCEVVizianagaramIndia
  2. 2.Department of Information TechnologyJNTUK-UCEVVizianagaramIndia

Personalised recommendations