CFP last date
22 April 2024
Reseach Article

Identification of Fingerprint using Discrete Wavelet Packet Transform

by Fahima Tabassum, Md. Imdadul Islam, M.R. Amin
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 7
Year of Publication: 2015
Authors: Fahima Tabassum, Md. Imdadul Islam, M.R. Amin
10.5120/ijca2015906613

Fahima Tabassum, Md. Imdadul Islam, M.R. Amin . Identification of Fingerprint using Discrete Wavelet Packet Transform. International Journal of Computer Applications. 128, 7 ( October 2015), 38-44. DOI=10.5120/ijca2015906613

@article{ 10.5120/ijca2015906613,
author = { Fahima Tabassum, Md. Imdadul Islam, M.R. Amin },
title = { Identification of Fingerprint using Discrete Wavelet Packet Transform },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 7 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number7/22889-2015906613/ },
doi = { 10.5120/ijca2015906613 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:20:49.404052+05:30
%A Fahima Tabassum
%A Md. Imdadul Islam
%A M.R. Amin
%T Identification of Fingerprint using Discrete Wavelet Packet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 7
%P 38-44
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Objective of this paper is to identify a person taking fingerprint as a biometric parameter using wavelet packet transform. Here both conventional discrete wavelet transform (DWT) and discrete wavelet packet transform (WPT) are used considering special basis function/matrix to extract the coefficients of basis functions those convey the most of the energy of the signal or image. Here top 5% coefficients are chosen which actually convey the characteristics of an image. The outcome of the paper is to determine the set of energetic coefficients of basis functions which carry the features of an image hence storage required to preserve the template of images will be reduced considerably.

References
  1. De Stefano and P. R. White, “Training Methods for Image Noise Level Estimation onWavelet Components,” EURASIP Journal on Applied Signal Processing 2004:16, 2400–2407
  2. Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, Third edition, Pearson education, 2013
  3. Jagannath Sethi1, Sibaram Mishra, Prajna Parimita Dash, Sudhansu Kumar Mishra and Sukadev Meher, “Image Compression Using Wavelet Packet Tree,” ACEEE Int. J. on Signal & Image Processing, Vol. 02, No. 01, Jan 2011, pp.41-43.
  4. G. K. Kharate and V. H. Patil, “Color Image Compression Based On Wavelet Packet Best Tree,” IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 3, pp.31-35, March 2010
  5. Deng Wang , Duoqian Miao and Chen Xie, “Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection,” Expert Systems with Applications, Elsevier Ltd, vo.38, pp.14314-14320, 2011
  6. Aguerrebere, G. Capdehourat, M. Delbracio, M. Mateu, A. Fern´andez and F. Lecumberry. “An Improved Face Recognition Algorithm through Gabor Filter Adaptation,” Automatic Identification Advanced Technologies, 2007 IEEE Workshop, pp.74-79, June 2007
  7. Muhammad Sharif, Adeel Khalid, Mmudassar Raza and Sajjad Mohsin, “Face Recognition using Gabor Filters,” Journal of Applied Computer Science & Mathematics, no. 11 (5),.pp.53-58, 2011, Suceava
  8. C. Aguerrebere, G. Capdehourat, M. Delbracio, M. Mateu, A. Fern´andez and F. Lecumberry. “An Improved Face Recognition Algorithm through Gabor Filter Adaptation,” Automatic Identification Advanced Technologies, 2007 IEEE Workshop, pp.74-79, June 2007
  9. Tsai, J. S. Taur and C. W. Tao, “Iris Recognition Using Gabor Filters and the Fractal Dimension,” Journal of Information Science and Engineering 25, 633-648 (2009)
  10. M. Rajalakshmi and P. Subashini, “Texture Based Image Segmentation of Chili Pepper X-Ray Images Using Gabor Filter,” International Journal of Advanced Studies in Computer Science & Engineering IJASCSE, vol. 3, pp.44-51, Issue 3, 2014
  11. Wan Azizun Wan Adnan, Lim TZE Siang and Salasiah Hitam, “Fingerprint recognition in wavelet domain,” Journal Teknologi, 41(D), Universiti Teknologi Malaysia, pp. 25-42, Dis. 2004.
  12. M. Tico, P. Kuosmanen and J. Saarinen, “Wavelet domain features for fingerprint recognition,” IEE Electronics Letters, Vol.37, No. 1, pp.21-22, 4th January, 2001.
  13. Anil K. Jain, Yi Chen and Malter Demirkus, “Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 1, pp. 15-27, January 2007.
  14. K. Thaiyalnayaki, S. Syed Abdul Karim and P. Varsha Parmar, “Finger print recognition using Discrete Wavelet Transforn,” International Journal of Computer Applications (0975-8887), Vol. 1, No. 24, pp. 96-100, 2010.
  15. Seok Won Lee and Boo Hee Nam, “Fingerprint Recognition Using Wavelet Transform and Probabilistic Neural Network,” International Joint Conference on Neural Networks, 1999. IJCNN’99, Vol. 5, DOI: 10.1109/IJCNN.1999.836183, pp. 3276-3279, July 10-16, 1999, Wahsington, DC, USA.
  16. Dhruv Batra, Girish Singhal and Santanum Chaudhury, “Gabor Filter based Fingerprint Classification using Support Vector Machines,” IEEE India Annual Conference 2004, pp. 256-261, DECEMBER 20-22, INDICON 2004.
  17. Chih-Jen Lee and Sheng-De Wang, “Fingerprint feature extraction using Gabor filters,” Electronics Letters, vol.35, no. 4, pp.288-290, February 18, 1999.
  18. G. Sambasiva Rao, C. Naga Raju, L.S.S Reddy and E. V. Prasad, “A Novel Fingerprints Identification System Based on The Edge Detection,” IJCSNS International Journal of Computer Science and Network Security, Vol. 8, No. 12, pp. 394.397, December 2008.
  19. Brian DeCann, Bozhao Tan and Stephanie Schuckers, “A Novel Region Based Liveness Detection Approach for Fingerprint Scanners,” Advances Biometrics, Volume 5558, pp 627-636, 2009
  20. Josef Strom Bartunek, Mikael Nilsson, Jorgen Nordberg and Ingvar Claesson, “Neural Network based Minutiae Extraction from Skeletonized Fingerprints,” IEEE Region 10 Conference 2006, TENCON 2006, pages 1-4, DOI: 10.1109/TENCON.2006.344104, November 14-17, 2006, Hong Kong.
Index Terms

Computer Science
Information Sciences

Keywords

Signal space scaling and shifting parameter basis function concentrator vector and filter bank.