Fingerprint Authentication System Based on Minutiae and Direction Field Toning Technique

  • S. Valarmathy
  • M. Arunkumar
  • M. Padma
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)


Fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify the individuals and verify their identity. Minutiae points are local ridge characteristics which can provide unique information. The conventional methods have utilized this minutiae information only as a point set. As a global feature, orientation field has extremely higher inter-personal variation than that of minutiae points. This proposed method describes the implementation of an Automatic Fingerprint Identification System (AFIS) which incorporates both local and global features of the fingerprints and operates in three stages: (1) minutiae extraction (2) reconstruction of orientation field and (3) fusion matching. An Improved Gabor filter algorithm is used for reliable minutiae extraction. From this extracted minutiae, the orientation field is reconstructed by using Interpolation-Model based (IM) method and utilized in the matching stage. This reconstructed orientation field matching is then fused with the minutiae-based matching. Thus the proposed scheme can enhance the performance of the system and obtain better matching accuracy than conventional methods.


Decision-level fusion Interpolation Minutiae Orientation field Polynomial model 


  1. 1.
    Gu J, Zhou CJ, Yang C (2006) Fingerprint recognition by combining global structure and local cues. IEEE Trans Image Process 15:1952–1964Google Scholar
  2. 2.
    Gu J, Zhou J, Zhang D (2004) A combination model for orientation field of fingerprints. Pattern Recog 37:543–553Google Scholar
  3. 3.
    Jain AK, Hong L, Bolle R (1997) On-line fingerprint verification. IEEE Trans Pattern Anal Mach Intell 19:302–314Google Scholar
  4. 4.
    Jain AK, Prabhakar S, Hong L (1999) A multichannel approach to fingerprint classification. IEEE Trans Pattern Anal Mach Intell 21:348–359Google Scholar
  5. 5.
    Pankanti S, Prabhakar S, Jain AK (2002) On the individuality of fingerprints. IEEE Trans Pattern Anal Mach Intell 24:1010–1025CrossRefGoogle Scholar
  6. 6.
    Qi J, Yang S, Wang Y (2005) Fingerprint matching combining the global orientation field with minutia. Pattern Recogn Lett 26(15):2424–2430CrossRefGoogle Scholar
  7. 7.
    Ross A, Shah J, Jain AK (2007) From template to image: reconstructing fingerprints from minutia points. IEEE Trans Pattern Anal Mach Intell 29:544–560CrossRefGoogle Scholar
  8. 8.
    Weldon TP, Higgins WE, Dunn DF (1996) Efficient Gabor filter design for texture segmentation. Pattern Recogn 29(12):2005–2015CrossRefGoogle Scholar
  9. 9.
    Zhou J, Gu J (2004) A model-based method for the computation of fingerprints orientation field. IEEE Trans Image Process 13:821–835CrossRefGoogle Scholar
  10. 10.
    Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2002) FVC2002: second fingerprint verification competition. In: Proceeding international conference on pattern recognition, August 2002, vol 3, pp 811–814 Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Bannari Amman Institute of TechnologyErodeIndia

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