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Fingerprint Databases for Technology Evaluation, Characterization and Measurement of Difficulty

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Synonyms

Difficulty level of a fingerprint dataset

Definition

ISO/IEC TR 29198, Characterization and measurement of difficulty for fingerprint databases for technology evaluation, about to be published as an ISO/IEC Technical Report as of the time of this writing [1] defines the level of difficulty (LOD) as a relative measure of a fingerprint dataset which represents how “challenging” or “stressing” the dataset is for recognition compared to other datasets. In other words, it represents how difficult it is to achieve better recognition accuracy within the specific dataset. The computation of LOD is based on factors such as relative sample quality, common area (or overlapping region), and deformation between a pair of mated fingerprint impressions. When agglomerated for the entire mated pairs in a dataset, the LOD can be used for characterizing and measuring the difficulty level of the fingerprint dataset used in technology evaluation [1, 2].

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References

  1. ISO/IEC JTC 1/SC 37, ISO/IEC TR 29198 – Information technology - Characterization and measurement of difficulty for fingerprint databases for technology evaluation

    Google Scholar 

  2. S. Li, H. Kim, C. Jin, S. Elliott, M. Ma, Assessing the level of difficulty of fingerprint datasets based on relative quality measures. Inf. Sci. (Elsevier, 2013). http://dx.doi.org/10.1016/j.ins.2013.05.025

  3. F. Alonso-Fernandez et al., A comparative study of fingerprint image-quality estimation methods. IEEE Trans. Inf. Forensics Secur. 2, 734–743 (2007)

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  4. R. Cappelli, D. Maio, D. Maltoni, J.L. Wayman, A.K. Jain, Performance evaluation of fingerprint verification systems. IEEE Trans. Pattern Anal. Mach. Intell. 28, 3–18 (2006)

    Google Scholar 

  5. Y. Chen, S.C. Dass, A.K. Jain, Fingerprint quality indices for predicting authentication performance, in Proceedings of 5th international Conference on Audio and Video-based Biometric Person Authentication, Hilton Rye Town, 2005, pp. 160–170

    Google Scholar 

  6. P. Grother, E. Tabassi, Performance of biometric quality measures. IEEE Trans. Pattern Anal. Mach. Intell. 29, 531–43 (2007)

    Google Scholar 

  7. R.A. Hicklin, C.L. Reedy, Implications of the IDENT/IAFIS image quality study for visa fingerprint processing. Technical report, Mitretek Systems Inc., 31 Oct 2002

    Google Scholar 

  8. C. Jin, H. Kim, S. Elliott, Matching performance-based comparative study of fingerprint sample quality measures. J. Korea Inst.Inf. Secur. Cryptol. 19, 11–25 (2009)

    Google Scholar 

  9. E. Lim, X. Jiang, W. Yau, Fingerprint quality and validity analysis, in Proceedings of 2002 International Conference on Image Processing, Rochester, vol. 1, 2002, pp. 469–472

    Google Scholar 

  10. R.E. Walpole, R.H. Myers, S.L. Myers, K. Ye, Probability & Statistics for Engineers & Scientists, 8th edn. (Pearson Education Inc., Upper Saddle River, 2007)

    Google Scholar 

  11. C. Jin, H. Kim, Pixel-level singular point detection from multi-scale Gaussian filtered orientation field. Pattern Recognit. 43, 3879–3890 (2010)

    MATH  Google Scholar 

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Kim, H., Li, S. (2015). Fingerprint Databases for Technology Evaluation, Characterization and Measurement of Difficulty. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_9049

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