Fingerprint Matching Based on Orientation Feature

Abstract:

This paper presents a fast and reliable algorithm for fingerprint verification. Our proposed fingerprint verification algorithm is based on image-based fingerprint matching. The improved orientation feature vector of two fingerprints has been compared to compute the similarities at a given threshold. Fingerprint image has been aligned by rotating through an angle before feature vector is computed and matched. Row and Column variance feature vector of orientation image will be employed. The algorithm has been tested on the FVC2002 Databases. The performance of algorithm is measured in terms of GAR and FAR. At a threshold level of 1.1 % and at 5.7% FAR the GAR observed is 97.83%. The improved Feature vector will lower imposter acceptance rate at reasonable GAR and hence yields better GAR at lower FAR. The proposed algorithm is computationally very efficient and can be implemented on Real-Time Systems.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 888-894
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.888
• Online since: November 2011
• Authors: Ravinder Kumar, Pravin Chandra, M. Hanmandlu
• Keywords: False Acceptance Rate, Feature Vector, Fingerprint Verification, Genuine Acceptance Rate, Image-Based Matching

References

[1] J. V Kulkarni, B. D Patil, R . R . S Holambe (2006) Orientation feature for fingerprint matching, , Pattern Recognition, vol 39 (2006) 1551 – 1554.

DOI: 10.1016/j.patcog.2006.03.007

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

[3] 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

[4] A. K. Jain, S. Prabhakar and S. Pankanti. Filterbank – based fingerprint matching, IEEE Transactions on Image Processing, vol. 9, pp.846-859, May (2000).

DOI: 10.1109/83.841531

[5] Anil Jain, Lin Hong, and Ruud Bolle, On-Line Fingerprint Verification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 4, April (1997).

DOI: 10.1109/34.587996

[6] A. M. Bazen and S. H. Gerez, Systematic methods for the computation of the directional fields and singular points of fingerprints, IEEE Trans Pattern Anal. Mach. Intell., vol. 24, no. 7, p.905–919, (2002).

DOI: 10.1109/tpami.2002.1017618

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

[8] Ling Hong, Yifei Wan, and Anil Jain, Fingerprint Image Enhancement: Algorithm and Performance Evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, August (1998).

DOI: 10.1109/34.709565

[9] Jie Zhou, Fanglin 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

[10] Tejas Joshi, Somnath Dey, Debasis Samanta, A Two-stage Algorithm for Core Point Detection in Fingerprint Images, TENCON 2009 - 2009 IEEE Region 10 Conference, Publication Year: 2009 , Page(s): 1 – 6.

DOI: 10.1109/tencon.2009.5396214

[11] K. Ito, H. Nakajima, K. Kobayashi, T. Aoki, and T. Higuchi, A Fingerprint Matching Algorithm Using Phase-Only Correlation, IEICE TRANS. FUNDAMENTALS, vol. E87-A, pp.682-691, (2004).

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

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