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Verification and Validation of a Fingerprint Image Registration Software

Abstract

The need for reliable identification and authentication is driving the increased use of biometric devices and systems. Verification and validation techniques applicable to these systems are rather immature and ad hoc, yet the consequences of the wide deployment of biometric systems could be significant. In this paper we discuss an approach towards validation and reliability estimation of a fingerprint registration software. Our validation approach includes the following three steps: (a) the validation of the source code with respect to the system requirements specification; (b) the validation of the optimization algorithm, which is in the core of the registration system; and (c) the automation of testing. Since the optimization algorithm is heuristic in nature, mathematical analysis and test results are used to estimate the reliability and perform failure analysis of the image registration module.

References

  1. 1.

    Jain AK, Pankanti S: Automated fingerprint identification and imaging systems. In Advances in Fingerprint Technology. 2nd edition. Edited by: Lee HC, Gaensslen RE. CRC Press, Boca Raton, Fla, USA; 2001.

    Google Scholar 

  2. 2.

    Jain AK, Ross A, Prabhakar S: Fingerprint matching using minutiae and texture features. Proceedings of International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 3: 282–285.

    Google Scholar 

  3. 3.

    Prabhakar S, Wang J, Jain AK, Pankanti S, Bolle R: Minutiae verification and classification for fingerprint matching. Proceedings of the 15th International Conference on Pattern Recognition (ICPR '00), September 2000, Barcelona, Spain 1: 25–29.

    Article  Google Scholar 

  4. 4.

    Thévenaz P, Ruttimann UE, Unser M: A pyramid approach to subpixel registration based on intensity. IEEE Transactions on Image Processing 1998, 7(1):27–41. 10.1109/83.650848

    Article  Google Scholar 

  5. 5.

    Desovski D, Gandikota V, Liu Y, Jiang Y, Cukic B: Validation and reliability estimation of a fingerprint image registration software. Proceedings of the 15th International Symposium on Software Reliability Engineering (ISSRE '04), November 2004, Bretagne, France 306–313.

    Google Scholar 

  6. 6.

    Press WH, Flannery BP, Teukolsky SA, Vetterling WT (Eds): Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, UK; 1988–1992.

    Google Scholar 

  7. 7.

    Roweis S: Levenberg-Marquardt Optimization. https://doi.org/www.cs.toronto.edu/~roweis/notes.html

  8. 8.

    Chaar JK, Halliday MJ, Bhandari IS, Chillarege R: In-process evaluation for software inspection and test. IEEE Transactions on Software Engineering 1993, 19(11):1055–1070. 10.1109/32.256853

    Article  Google Scholar 

  9. 9.

    Fisher MS, Cukic B: Automating techniques for inspecting high assurance systems. Proceedings of the 6th IEEE International Symposium on High Assurance Systems Engineering (HASE '01), October 2001, Boco Raton, Fla, USA 117–126.

    Google Scholar 

  10. 10.

    Unser M: Splines: a perfect fit for signal and image processing. IEEE Signal Processing Magazine 1999, 16(6):22–38. 10.1109/79.799930

    Article  Google Scholar 

  11. 11.

    Unser M, Aldroubi A, Eden M: B-spline signal processing. I. Theory. IEEE Transactions on Signal Processing 1993, 41(2):821–833. 10.1109/78.193220

    Article  Google Scholar 

  12. 12.

    Unser M, Aldroubi A, Eden M: B-spline signal processing. II. Efficiency design and applications. IEEE Transactions on Signal Processing 1993, 41(2):834–848. 10.1109/78.193221

    Article  Google Scholar 

  13. 13.

    Unser M, Aldroubi A, Eden M:The-polynomial spline pyramid. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993, 15(4):364–379. 10.1109/34.206956

    Article  Google Scholar 

  14. 14.

    comp.ai.neural-nets FAQ, https://doi.org/www.faqs.org/faqs/ai-faq/neural-nets/

  15. 15.

    Marquardt DW: An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics 1963, 11: 431–441. 10.1137/0111030

    MathSciNet  Article  Google Scholar 

  16. 16.

    Yamashita N, Fukushima M: On the rate of convergence of the Levenberg-Marquardt method. Computing 2001, ([Suppl] 15):227–238.

    Google Scholar 

  17. 17.

    Turbo Registration plug-in, https://doi.org/bigwww.epfl.ch/thevenaz/turboreg/

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Correspondence to Dejan Desovski.

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Desovski, D., Gandikota, V., Liu, Y. et al. Verification and Validation of a Fingerprint Image Registration Software. EURASIP J. Adv. Signal Process. 2006, 015940 (2006). https://doi.org/10.1155/ASP/2006/15940

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Keywords

  • Optimization Algorithm
  • Source Code
  • Quantum Information
  • Requirement Specification
  • Image Registration