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Uniformity Level Approach to Fingerprint Ridge Frequency Estimation

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 61 - Number 22
Year of Publication: 2013
Authors:
Iwasokun Gabriel Babatunde
Akinyokun Oluwole Charles
Olabode Olatunbosun
10.5120/10229-4809

Iwasokun Gabriel Babatunde, Akinyokun Oluwole Charles and Olabode Olatunbosun. Article: Uniformity Level Approach to Fingerprint Ridge Frequency Estimation. International Journal of Computer Applications 61(22):26-32, January 2013. Full text available. BibTeX

@article{key:article,
	author = {Iwasokun Gabriel Babatunde and Akinyokun Oluwole Charles and Olabode Olatunbosun},
	title = {Article: Uniformity Level Approach to Fingerprint Ridge Frequency Estimation},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {61},
	number = {22},
	pages = {26-32},
	month = {January},
	note = {Full text available}
}

Abstract

Stages of fingerprint image enhancement include segmentation, normalization, filtering, binarization and filtering. Each of these stages has proved to be very essential for achieving a well enhanced fingerprint image. The major prerequisites to filtering a fingerprint image are ridge orientation and frequency estimations. While ridge orientation estimation is done to obtain the orientation of the ridges, ridge frequency estimation is done with a view to ascertaining the number of ridges within a unit length. The number is useful for fingerprint image filtering. In this paper, a modified fingerprint ridge frequency estimation algorithm is implemented. The modified algorithm consists of stages for estimating ridge orientation and uniformity levels. Two types of images; namely synthetic and real fingerprints were used to evaluate the performance of the algorithm. The results of the evaluation reveal that the modified algorithm shows greater speed and effectiveness than its original version. Facts also emerged on the basic characteristics of the estimates.

References

  • Roberts C. 2005. Biometrics' (http://www. ccip. govt. nz/newsroom/information_notes/2005/biometrics. pdf). Accessed 12/07/2006
  • Anil K. J. , Jianjiang F and Karthik N. 2010. Fingerprint Matching, IEEE Computer Society, page 36-44
  • Iwasokun G. B. , Akinyokun O. C. , Alese B. K. & Olabode O. 2011. Adaptive and Faster Approach to Fingerprint Minutiae Extraction and Validation. International Journal of Computer Science and Security, Malaysia, Volume 5 Issue 4, page 414-424.
  • Iwasokun G. B. , Akinyokun O. C. , Alese B. K. & Olabode O. 2012. Fingerprint Image Enhancement: Segmentation to Thinning, International Journal of Advanced Computer Science and Applications (IJACSA), Indian, Vol. 3, No. 1, 2012
  • Hong L. , Wau Y. and Anil J. 2006. Fingerprint image enhancement: Algorithm and performance evaluation, Pattern Recognition and Image Processing Laboratory, Department of Computer Science, Michigan State University, pp1-30
  • Raymond T. 2003. Fingerprint Image Enhancement and Minutiae Extraction, PhD Thesis Submitted to School of Computer Science and Software Engineering, University of Western Australia, pp21-56.
  • Yilong Y. , Jie T. and Xiukun Y. 2002. Ridge Distance Estimation in Fingerprint Images: Algorithm and Performance Evaluation, EURASIP Journal on Applied Signal Processing Vol. 4, 495–502
  • Hong, L. , Wan, Y. & Jain, A. 1998. Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20: 777–789
  • Arun Vinodh C. 2007. Extracting and Enhancing the Core Area in Fingerprint Images, IJCSNS International Journal of Computer Science and Network Security, VOL. 7 No. 11, pp16-20
  • Greenberg S. Aladjem M. and Kogan D. 2002. Fingerprint Image Enhancement Using Filtering Techniques, Elsevier Science Ltd, pg 227-236
  • Byung-Gyu K. , Han-Ju K. and Dong-Jo P. 2002. New Enhancement Algorithm for Fingerprint Images, IEEE, 1051-4651/02 $17. 00
  • Devansh A. and Anoop N. 2011. Fingerprint Feature Extraction from Gray Scale Images by Ridge Tracing, IEEE, 978-1-4577-1359-0/11/$26. 00
  • Porwick P. and Wieclaw L. 2009: A new fingerprint ridge frequency determination method, IEICE Electronics Express, Vol. 6, No. 3 pages 154-160
  • Malickas A. and Rvitkus R. 2000. Fingerprint Pre-Classification Using Ridge Density, INFORMATICA, Institute of Mathematics and Informatics, Vilnius, Vol. 11, No. 3, 257–268 257
  • Bernsen J. 1986. Dynamic thresholding of grey-level images, in Proceedings of the 8th International Conference on Pattern Recognition, Paris, France, pp. 1251–1255.
  • Kovesi, P. 2002. MATLAB functions for computer vision and image analysis. School of Computer Science and Software Engineering, The University of Western Australia. http://www. cs. uwa. edu. au/pk/Research/MatlabFns/index. html. Accessed: 21 March 2009