Biometric Technologies Minutia based Fingerprint Matching using Linear Programming

1. Biometric TechnologiesMinutia based Fingerprint Matching using Linear Programming Presented by Ibrahim M Ismail

2. Outline • Introduction to Project • Background to Fingerprint Matching • Linear Program Design • Results • Comparison

3. Introduction • Use Linear Programming (LP) for minutiae based fingerprint matching. • Why LP ? • Rules for LP • No multiplication of variables • Just three things involved: • Data Sets • Linear Inequalities/Equalities • Maximization/Minimization Function (also Linear)

4. Notations

5. Translation

6. Rotation

7. Rotation

8. Matching

9. Matching

10. Maximization Function

11. Score Match: 10.65 Non-match: 7.97

12. Threshold Value

13. Threshold value

14. Other Techniques • Title: On-line fingerprint verification Authors: A. Jain and L. Hong Journal: Pattern Analysis and Machine Intelligence 1997 • Title: An efficient algorithm for fingerprint matching Authors: C. Wang, M. Gavrilova, Y. Luo and J. Rokne Conference: Proceedings of the 18th International Conference on Pattern Recognition, 2006 • Title: Fingerprint matching combining the global orientation field with minutia Authors: J. Qi, S. Yang and Y. Wang Journal: Pattern Recognition Letters 26 (15), 2005

15. On-Line Fingerprint Matching • FRR: 0.16% • FAR: 11.23% • Average: 5.70%

16. On-Line Fingerprint Matching • FRR: 5.46% • FAR: 0.84% • Average: 3.15%

17. Fingerprint Matching combining the global orientation field with Minutia • FAR: 3.01% • FRR: 12.43% • Average: 7.72%

18. Comparing

19. Critical Examination • Advanced Decision Making • Large Increase of Variable Size (loss of time) for accuracy • Rows/Inequalities Avg: 7,315 Max: 21,807 O(|M||N|+|M||K|+ |N||K|) • Columns/Variables Avg: 14,544 Max: 91,769 O(|M||N||K|)

20. Simplex Algorithm • George Bernard Dantzig • 1947 • Simplex • Brief outline • Exponential Worst Case • Binary Integer Programming NP Hard

21. Conclusion • Slow vs. Accurate • Not Flexible • To be fair… Should be judged against algorithms that use the similar matching criteria

22. References [1] Cappelli R., Maio D. and Maltoni D., Modeling Plastic Distortion in Fingerprint Images, ICAPR 2001, LNCS 2013, pp. 369-376, 2001. [2] Chengfeng Wang, Marina Gavrilova, Yuan Luo, Jon Rokne, An efficient algorithm for fingerprint matching, Proceedings of the 18th International Conference on Pattern Recognition - Volume 1, 2006, 1034-1037 [3] Fornefett M., Rohr K. and Stiehl H.S., Radial basis functions with compact support for elastic registration of medical images, Image and Vision Computing, no. 19, pp. 87-96, 2001. [4] FVC 2004 Fingerprint Verification Competition, Retrieved April 13, 2008, from the World Wide Web: http://bias.csr.unibo.it/fvc2004/ [5] GLPK (GNU Linear Programming Kit), Retrieved 13 April, 2008 from the World Wide Web: www.gnu.org/software/glpk/glpk.html [6] GNU MathProg, Retrieved April 13, 2008, from the World Wide Web: www.lpsolve.sourceforge.net/5.5/MathProg.htm

23. References [7] Greenberg, cites: V. Klee and G.J. Minty. "How Good is the Simplex Algorithm?" In O. Shisha, editor, Inequalities, III, pages 159–175. Academic Press, New York, NY, 1972 [8] Jain A.K., Hong L. and Bolle R., On-line fingerprint verification, PAMI, vol. 19, no. 4, pp. 302-314, 1997. [9] Maltoni D., Maio D., Jain A. K., and Prabhakar S. Handbook of Fingerprint Recognition. Springer-Verlag, New York, 2003. [10] The MathWorks, Retrieved April 13, 2008, from the World Wide Web: www.mathworks.com/ [ref11] Qi J., Yang S., Wang Y., Fingerprint matching combining the global orientation field with minutia, Pattern Recognition Lett. 26 (15) (2005) 2424–2430. [12] Wang C.F. and Hu Z.Y., Image Based Rendering under Varying Illumination, the Journal of High Technology Letters, vol. 9, no. 3, pp. 6-11, 2003.

24. THANK YOU! Questions?