Open Access Open Access  Restricted Access Subscription or Fee Access

Fingerprint Matching Using Ridge Features and Hamming Algorithm for Distorted Fingerprints

T. Indhumathi, P.C. Gopi, S. Savitha, R. Sharmila

Abstract


This paper proposes a new fingerprint matching system which uses both ridge features and conventional minutiae features. This system may increase the recognition performance of fingerprint matching system against skin distortions. This system suggests the extraction of ridge features like ridge count, ridge length, ridge curvature direction and ridge type. These ridge features are less affected by skin distortions. To extract the ridge features we use the ridge based coordinate system in the skeletonized image. With the proposed ridge features and conventional minutiae features we propose a matching procedure using the hamming algorithm. In the matching process we compute the Euclidian distance between the input fingerprint finger code and the finger code of the template. FVC 2002 was used as the sample database of fingerprints. Thus we conclude that the matching process using the proposed features and hamming algorithm may increase the recognition performance of the fingerprint recognition systems against distorted fingerprints.

Keywords


Distortion, FVC 2002, Hamming Algorithm, Ridge Features.

Full Text:

PDF

References


Heeseung Choi, Kyoungtaek Choi, and Jaihie Kim,”Fingerprint Matching Incorporating Ridge Features with Minutiae”, IEEE Trans. Inf. Forensics Security, vol.6, no. 2, June 2011.

D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, “Handbook of Fingerprint Recognition”. New York: Springer-Verlag, 2003.

A. Ross, S. Dass, and A. K. Jain, “A deformable model for fingerprint matching,” Pattern Recognit., vol. 38, no. 1, pp. 95–103, 2005.

Anil K. Jain, Salil Prabhakar, Lin Hong, and Sharath Pankanti, “FingerCode: A Filterbank for Fingerprint Representation and Matching”.

R. Appelli, D. Maio, and D. Maltoni, “ Modelling Plastic distortion in fingerprint images”, in Proc. ICAPR, 2001, pp. 369-376.

D. Lee, K. Choi, and J.Kim, “A robust fingerprint matching algorithm using local alignment,” in Proc. 16th Int. Conf. Pattern Recognition, Quebec City,

Que., Canada, Aug. 2002, vol. 3, pp. 803–806. Mar. 25, 2010 [Online]. Available: http://bias.csr.unibo.it/fvc2002

X. Chen, J. Tian, X. Yang, and Y. Zhang, “An algorithm for distorted fingerprint matching based on local triangle feature set,” IEEE Trans. Inf. Forensics Security, vol. 1, no. 2, pp. 169–177, Jun. 2006.

A. M. Bazen and S. H. Gerez, “Fingerprint matching by thin-plate spline modelling of elastic deformations,” Pattern Recognit., vol. 36, no. 8, pp. 1859–1867, Aug. 2003.

X. P. Luo, J. Tian, and Y.Wu, “Aminutia matching algorithm in fingerprint verification,” in Proc. 15th ICPR, Sep. 2000, vol. 4, pp. 833–836.

X. Jiang and W. Y. Yau, “Fingerprint minutiae matching based on the local and global structures,” in Proc. 15th Int. Conf. Pattern Recognition, Barcelona, Spain, Sep. 2000, vol. 2, pp. 1038–1041.

Z. M. Kovacs-Vajna, “A fingerprint verification system based on triangular matching and dynamic time warping,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 11, pp. 1266–1276, Nov. 2000.

R. Cappelli, A. Erol, D. Maio, and D. Maltoni, “Synthetic fingerprint- image generation,” in Proc. 15th Int. Conf. Pattern Recognition, Barcelona, Spain, Sep. 2000, pp. 3475–3478.

C. Lee, S. Lee, J. Kim, and S. Kim, “Preprocessing of a fingerprint image captured with a mobile camera,” in Proc. IAPR Int. Conf. Biometrics (ICB), Hong Kong, Jan. 2006, pp. 348–355, Springer LNCS- 3832.

D. Maio and D. Maltoni, “Ridge-line density estimation in digital images,” in Proc. 14th ICPR, 1998, vol. 1, pp. 534–538.

L. Hong, Y. Wan, and A. K. Jain, “Fingerprint image enhancement: Algorithm and performance evaluation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp. 777–789, Aug. 1998.

S. Lee, H. Choi, and J. Kim, “Fingerprint quality index using gradient components,” IEEE Trans. Inf. Forensics Security, vol. 3, no. 4, pp. 792–800, Dec. 2008.

K.V.Mardia and P. E. Jupp, Directional Statistics. New York:Wiley, 2000.

K. Choi, H. Choi, S. Lee, and J. Kim, “Fingerprint image mosaicking by recursive ridge mapping,” Special Issue Recent Adv. Biometrics Syst., IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 5, pp. 1191–1203, Oct. 2007.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.