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Reseach Article

An Efficient Fingerprint Denoiser for Fingerprint Recognition

by S. Uma Maheswari, E. Chandra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 22
Year of Publication: 2013
Authors: S. Uma Maheswari, E. Chandra
10.5120/11249-6421

S. Uma Maheswari, E. Chandra . An Efficient Fingerprint Denoiser for Fingerprint Recognition. International Journal of Computer Applications. 66, 22 ( March 2013), 29-32. DOI=10.5120/11249-6421

@article{ 10.5120/11249-6421,
author = { S. Uma Maheswari, E. Chandra },
title = { An Efficient Fingerprint Denoiser for Fingerprint Recognition },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 22 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number22/11249-6421/ },
doi = { 10.5120/11249-6421 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:23:07.909519+05:30
%A S. Uma Maheswari
%A E. Chandra
%T An Efficient Fingerprint Denoiser for Fingerprint Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 22
%P 29-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fingerprint identification is one of the oldest and popularly used forms of biometric identification system. Fingerprint image quality is of much importance to achieve high performance in Automatic Fingerprint Identification System (AFIS). Most of the fingerprint Recognition system relies on fingerprint ridges to extract the features. The fingerprint images which are corrupted due to variations in skin and impression conditions may reduce the efficiency of the feature extraction module. Therefore an efficient and a robust fingerprint image denoiser is necessary to overcome the challenges faced by the existing techniques. In this paper a new fingerprint image denoiser based on wavelets and contrast based grouping is proposed. The proposed model is compared with the existing algorithms. Experimental results proved that the proposed model is efficient in denoising the fingerprint image.

References
  1. Cheng, J. ; J. Tian, J. (2004): Fingerprint Enhancement with Dyadic Scale- Space, Pattern Recognition Letters, Vol. 25, pp. 1273-1284.
  2. Hong, L. ; Wan, Y. ; Jain, A. K. (1998): Fingerprint Image Enhancement: Algorithm and Performance Evaluation, Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp. 777-789.
  3. Huang, Z. ; Qi, F. (2000): Fingerprint Enhancement Based on MRF with Curve Accumulation, in Proc. of SPIE, Vol. 4552, pp. 45-50.
  4. Maio, D. ; Maltoni, D. (1997): Direct Gray-scale Minutiae Detection in Fingerprints, IEEE Trans. Pattern Anal. Mach. Intell. 19, pp. 27-39.
  5. Mallat, S. (1998): A Wavelet Tour of Signal Processing, Academic Press, New York.
  6. Maltoni, D. ; Maio, D. ; Jain, A. K. ; Prabhakar, S. (2003): Handbook of Fingerprint Recognition, Springer, New York, NY.
  7. Muresan, D. D. ; Parks, T. W. (2003): Adaptive principal components and image denoising, in: Proceedings of the 2003 International Conference on Image Processing, 14–17 September, vol. 1, pp. I101–I104.
  8. Prabhakar, S. ; Jain, A. K; Pankanti, S. (2003): Learning Fingerprint Minutiae Location and Type, Pattern Recognition, Vol. 36, No. 8, pp. 1847-1857.
  9. Pradenas, R. (1997): Directional Enhancement in the Frequency Domain of Fingerprint Images, in Proc. of SPIE, Vol. 2932, pp. 150-160.
  10. Shalash, W. M. ; Abou-Chadi, F. E. (2006): Fingerprint Image Enhancement with Dynamic Block Size, The 23rd National Radio Science Conference (NRSC), Mar. 14-16.
  11. Sherlock, B. G. ; Monro, D. M. ; Millard, K. ( 1994): Fingerprint Enhancement by Directional Fourier Filtering, Proc. of IEEE Visual Image Signal Process, Vol. 141, No. 2, pp. 87-94.
  12. Tahmasebi, A. M. ; Kasaei, S. (2002):"A Novel Adaptive Approach to Fingerprint Enhancement Filter Design, Signal Processing: Image Communication, Vol. 17, pp. 849-855.
  13. Tung, C. T. ( 2006):Fingerprint Image Enhancement Based on Teager Operator in The Wavelet Domain, Master thesis, Computer Science and Information Engineering, National Central University,
  14. Wu, C. ;Shi, Z. ; Govindaraju, V. ( 2004): Fingerprint Image Enhancement Method Using Directional Median Filter, Biometric Technology for Human Identification, Proc. of SPIE, Vol. 5404, pp. 66-75.
  15. Xudong Jiang. ; Wei-Yun Yau; Wee Ser(2001 ):Detecting the Fingerprint Minutiae by Adaptive Tracing the Gray-level Ridge, Pattern Recognition, pp. 999-1013.
  16. Yaroslavsky, L. P. (1985): Digital Signal Processing- An Introduction, Springer, Berlin.
  17. Yang, J. ; Liu, L. ; Jiang, T. ;Fan, Y. ( 2003):A Modified Gabor Filter Design Method for Fingerprint Image Enhancement, Pattern Recognition Letter 24, pp. 1805-1817
  18. Yu, Y. ; Acton, S. T. (2002): Speckle Reducing Anisotropic Diffusion, IEEE Trans. Image Processing, vol. 11, pp. 1260-1270.
  19. Zhang,L; Weisheng Dong; David Zhang;Guangming (2010): Two-stage image denoising by principal component analysis with local pixel grouping, Pattern Recognition 43, 1531–1549.
Index Terms

Computer Science
Information Sciences

Keywords

Fingerprint quality ridges AFIS wavelets contrast based grouping.