Fingerprint Singular Point Detection Using Orientation Field Reliability

Article Preview

Abstract:

Singular point detection is the most important step in Automatic Fingerprint Identification System (AFIS) and is used in fingerprint alignment, fingerprint matching, and particularly in classification. The computation of orientation field of a fingerprint can be verified by computing orientation field reliability. The most unreliable portion in orientation field can be the possible location of singular points. In this paper we have proposed a novel algorithm for detecting singular points using reliability of the fingerprint orientation field. Experimental results show that the proposed algorithm accurately detects singular points (core and delta) with the detection rate of 92.6 %.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

4499-4506

Citation:

Online since:

November 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] D. Maltoni, D. Maio, A. K. Jain and S. Prabhakar, Handbook of Fingerprint Recognition, Springer-Verlag, June (2009).

Google Scholar

[2] K. Karu and A. K. Jain, Fingerprint Classification, Pattern Recognition, vol. 17, no. 3, pp.389-404, (1996).

DOI: 10.1016/0031-3203(95)00106-9

Google Scholar

[3] Q. Zhang and H. Yan, Fingerprint Classification Based on Extraction and Analysis of Singurities and Pseudo Ridges, Pattern Recognition, vol. 37, no. 11, pp.2233-2243, (2004).

DOI: 10.1016/j.patcog.2003.12.020

Google Scholar

[4] M. Kawagoe, and A. Tojo, Fingerprint pattern classification, Pattern Recognition, Volume 17, Issue 3, pp.295-303, (1984).

DOI: 10.1016/0031-3203(84)90079-7

Google Scholar

[5] A. M. Bazen and S. H. Gerez, Systematic methods for the computation of the directional fields and singular points of fingerprints, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 24, no. 7, pp.905-919, (2002).

DOI: 10.1109/tpami.2002.1017618

Google Scholar

[6] A. K. Jain, S. Prabhakar, L. Hong , and S. Pankanti, Filterbank – based fingerprint matching, IEEE Transactions on Image Processing, vol. 9, p.846 – 859, (2000).

DOI: 10.1109/83.841531

Google Scholar

[7] Jie Zhou, Franglin Chen, and Jinwei Gu, A Novel Algorithm for Detecting Singular Points from Fingerprint Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, No. 7, July (2009).

DOI: 10.1109/tpami.2008.188

Google Scholar

[8] L. Hong, Y. Wan, and A. K. Jain, Fingerprint image enhancement: algorithm and performance evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, No. 7, July (2009).

DOI: 10.1109/34.709565

Google Scholar

[9] Sen Wang and Yangsheng Wang., Fingerprint Enhancement in the Singular Point Area, IEEE signal Processing letters, vol. 11, no. 1, pp.16-19, January (2004).

DOI: 10.1109/lsp.2003.819351

Google Scholar

[10] A. Jain, L. Hong, and R. Bolle , On-Line Fingerprint Verification, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp.302-314, (1997).

DOI: 10.1109/34.587996

Google Scholar

[11] A. K. Jain, F. Farrokhnia, Unsupervised Texture Segmentation Using Gabor Filters, Pattern Recognition, vol. 2, no. 12, p.1, 167-1, 186, (1991).

DOI: 10.1016/0031-3203(91)90143-s

Google Scholar

[12] Fingerprint Verification Competition (FVC), http: /bias. csr. unibo. it/fvc2002.

Google Scholar

[13] R. C. Gonzalez and R. E. Woods, Digital image processing, 3rd ed., Prentice Hall, Upper Saddle River, NJ.

Google Scholar

[14] A. R. Rao, A taxonomy for texture Description and Identification, Springer, New York, (1990).

Google Scholar