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Likelihood ratio data to report the validation of a forensic fingerprint evaluation method
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“In conclusion, for this work and according to the studied literature, the best indicators of both discrimination and calibration performances are Cllr and the ECE plots (explained in 3.3.1)[36,38] which give a good view of both the calibration and discrimination power of the forensic experts and the automated systems.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [38]
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Research works printed but not linked: 12
- Meuwly, 2016, A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation, Forensic Sci. Int.
- 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
- 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.
- 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.
- 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
- 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
- Brümmer N, Swart A. Bayesian calibration for forensic evidence reporting. arXiv preprint arXiv:1403.5997. 2014 Mar 24 · DOI
- 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
- Hepler, 2012, Score-based likelihood ratios for handwriting evidence, Forensic Sci. Int., 219, 129, 10.1016/j.forsciint.2011.12.009 · DOI
- D.Meuwly, Reconnaissance de Locuteurs en Sciences Forensiques: L’apport d’une Approche Automatique, (Ph.D. thesis), 2001.
- Meuwly, 2006, Forensic individualization from biometric data, Sci. Justice, 46, 205, 10.1016/S1355-0306(06)71600-8 · DOI
- Haraksim, 2016, Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions, IET Biom.
Cited by
Sorted by citations
Calibration of score based likelihood ratio estimation in automated forensic facial image comparison
Rodriguez, A. M., Geradts, Z., & Worring, M. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111239 · Cited by 19
Metadata
- Authors
- Ramos, D., Haraksim, R., & Meuwly, D.
- Venue
- Data in Brief
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 10
- Pages
- 75-92
- ISSN
- 2352-3409
- PMC
- PMC5144651
- Cited by
- 34
- References
- 0
- Language
- en
- Funders
- BBfor2 – European Commission Marie Curie Initial Training Network (FP7-PEOPLE-ITN-2008, 238803)
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