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Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

Ramos, Daniel, Haraksim, Rudolf, Meuwly, Didier·2017·Data in Brief
DOI 10.1016/j.dib.2016.11.008PMC PMC5144651
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Crossref · 10.1016/j.dib.2016.11.008

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Figures

Fig. 1 Three different regions of the scores produced by the AFIS algorithm (published in [1]).
Fig. 1 Three different regions of the scores produced by the AFIS algorithm (published in [1]).CC BY · PubMed Central
Fig. 2 ECE plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.
Fig. 2 ECE plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.CC BY · PubMed Central
Fig. 3 ECE plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.
Fig. 3 ECE plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.CC BY · PubMed Central
Fig. 4 DET plots of the KDE baseline method and the Multimodal method on the development dataset and the DET plot of the Multimodal method on the validation dataset.
Fig. 4 DET plots of the KDE baseline method and the Multimodal method on the development dataset and the DET plot of the Multimodal method on the validation dataset.CC BY · PubMed Central
Fig. 5 ECE plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.
Fig. 5 ECE plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.CC BY · PubMed Central
Fig. 6 Tippett plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.
Fig. 6 Tippett plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.CC BY · PubMed Central
Fig. 7 ECE plots of the Multimodal method on the validation dataset in the varying minutiae configurations (published in [3]).
Fig. 7 ECE plots of the Multimodal method on the validation dataset in the varying minutiae configurations (published in [3]).CC BY · PubMed Central
Fig. 8 DET plots of the Multimodal method on the validation dataset in the varying minutiae configurations (published in [3]).
Fig. 8 DET plots of the Multimodal method on the validation dataset in the varying minutiae configurations (published in [3]).CC BY · PubMed Central
Fig. 9 ECE plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.
Fig. 9 ECE plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.CC BY · PubMed Central
Fig. 10 DET plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.
Fig. 10 DET plots of the KDE baseline method and the Multimodal method on the development dataset and the ECE plot of the Multimodal method on the validation dataset.CC BY · PubMed Central

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Research works printed but not linked: 12

  1. Meuwly, 2016, A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation, Forensic Sci. Int.
  2. R.Haraksim, D.Ramos, D.Meuwly, C.E.H.Berger, Measuring coherence of computer-assisted likelihood ratio methods, Forensic Sci International, In press, 〈http://dx.doi.org/10.1016/j.forsciint.2015.01.033〉 · DOI
  3. D. Meuwly, R.G.F. Veldhuis. Forensic biometrics: from two communities to one discipline, in: Proceedings of the BIOSIG International Conference of the Biometrics Special Interest Group, 2012, pp. 207–218.
  4. T.Ali, L.J.Spreeuwers, R.N.J.Veldhuis, A review of calibration methods for biometric systems in forensic applications, in: Proceedings of the 33rd WIC Symposium on Information Theory in Benelux, Boekelo, Netherlands, pp. 126–133, WIC. ISBN 978-90-365-3383-6, May, 2012.
  5. Egli Anthonioz, 2014, Evidence evaluation in fingerprint comparison and automated fingerprint identification systems—Modeling between finger variability, Forensic Sci. Int., 235, 86, 10.1016/j.forsciint.2013.12.003 · DOI
  6. Alberink, Ivo, Arent Jongh, and Crystal Rodriguez. Fingermark evidence evaluation based on automated fingerprint identification system matching scores: the effect of different types of conditioning on likelihood ratios. Journal of forensic sciences 59.1 (2014): 70-81 · DOI
  7. Brümmer N, Swart A. Bayesian calibration for forensic evidence reporting. arXiv preprint arXiv:1403.5997. 2014 Mar 24 · DOI
  8. Rodriguez, 2012, Introducing a semi-automated method to simulate a large number of forensic fingermarks for research on fingerprint identification, J. Forensic Sci., 57, 334, 10.1111/j.1556-4029.2011.01950.x · DOI
  9. Hepler, 2012, Score-based likelihood ratios for handwriting evidence, Forensic Sci. Int., 219, 129, 10.1016/j.forsciint.2011.12.009 · DOI
  10. D.Meuwly, Reconnaissance de Locuteurs en Sciences Forensiques: L’apport d’une Approche Automatique, (Ph.D. thesis), 2001.
  11. Meuwly, 2006, Forensic individualization from biometric data, Sci. Justice, 46, 205, 10.1016/S1355-0306(06)71600-8 · DOI
  12. Haraksim, 2016, Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions, IET Biom.

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Metadata

Authors
Ramos, D., Haraksim, R., & Meuwly, D.
ORCID
Venue
Data in Brief
Publisher
Elsevier BV
Published
2017-02
Volume
10
Pages
75-92
ISSN
2352-3409
DOI
10.1016/j.dib.2016.11.008
PMC
PMC5144651
Cited by
34
References
0
Language
en
Licence
Funders
BBfor2 – European Commission Marie Curie Initial Training Network (FP7-PEOPLE-ITN-2008, 238803)

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