Cited by 18

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From Biometric Scores to Forensic Likelihood Ratios

Ramos, Daniel, Krish, Ram P., Fierrez, Julian, Meuwly, Didier·2017·Advances in Computer Vision and Pattern Recognition

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Crossref · 10.1007/978-3-319-50673-9_14

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Research works printed but not linked: 42

  1. Saks MJ, Koehler JJ (2005) The coming paradigm shift in forensic identification science. Science 309(5736):892–895 · DOI
  2. Cook R, Evett IW, Jackson G, Jones PJ, Lambert JA (1998) A model for case assessment and interpretation. Sci Justice 38:151–156 · DOI
  3. Aitken CGG, Taroni F (2004) Statistics and the evaluation of evidence for forensic scientists. Wiley, Chichester · DOI
  4. Berger CA, Champod JS, Curran C, Dawid J, Kloosterman AP (2011) Expressing evaluative opinions: a position statement. Sci Justice 51:1–2. Several signatories · DOI
  5. Ramos D (2007) Forensic evaluation of the evidence using automatic speaker recognition systems. PhD thesis, Depto. de Ingenieria Informatica, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain. http://atvs.ii.uam.es
  6. Brümmer N, du Preez J (2006) Application independent evaluation of speaker detection. Comput Speech Lang 20(2–3):230–275 · DOI
  7. van Leeuwen D, Brümmer N (2007) An introduction to application-independent evaluation of speaker recognition systems. In: Müller C (ed) Speaker classification. Lecture notes in computer science/Artificial intelligence, vol 4343. Springer, Heidelberg, Berlin, New York · DOI · Paper page
  8. Ramos D, Gonzalez-Rodriguez J (2013) Reliable support: measuring calibration of likelihood ratios. Forensic Sci Int 230:156–169 · DOI · Paper page
  9. Zadora G, Ramos D (2010) Evaluation of glass samples for forensic purposes–an application of likelihood ratio model and information-theoretical approach. Chemometr Intell Lab Syst 102:62–63 · DOI · Paper page
  10. Li P, Fu Y, Mohammed U, Elder J, Prince SJD (2010) Probabilistic models for inference about identity. IEEE Trans Pattern Anal Mach Intell (PAMI) 34(1):144–157
  11. Dehak N, Kenny P, Dehak R, Dumouchel P, Ouellet P (2010) Front-end factor analysis for speaker verification. IEEE Trans Audio Speech Lang Process 19(4):788–798 · DOI · Paper page
  12. Villalba J, Brümmer N (2011) Towards fully Bayesian speaker recognition: integrating out the between-speaker covariance. Proceedings of the 12th annual conference of the international speech communication association, Interspeech 2011. Florence, Italy, pp 505–508
  13. Zadora G, Martyna A, Ramos D, Aitken C (2014) Statistical analysis in forensic science: evidential values of multivariate physicochemical data. Wiley · DOI
  14. Rodriguez CM, de Jongh A, Meuwly D (2013) Introducing a semiautomatic method to simulate large numbers of forensic fingermarks for research on fingerprint identification. J Forensic Sci 57(2):334–342 · DOI
  15. van Leeuwen DA, Brümmer N (2013) The distribution of calibrated likelihood-ratios in speaker recognition. arXiv preprint · DOI
  16. Ramos D, Gonzalez-Rodriguez J, Zadora G, Aitken C (2013) Information-theoretical assessment of the performance of likelihood ratio models. J Forensic Sci 58:1503–1518 · DOI
  17. Haraksim R, Ramos D, Meuwly D, Berger CE (2015) Measuring coherence of computer-assisted likelihood ratio methods. Forensic Sci Int 249:123–132 · DOI
  18. Meuwly D, Ramos D, Haraksim R (in press) A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation. Forensic Sci Int. 10.1016/j.forsciint.2016.03.048 · DOI · Paper page
  19. Taroni F, Aitken C, Garbolino P, Biedermann A (2006) Bayesian networks and probabilistic inference in forensic science. Wiley · DOI
  20. Cook R, Evett IW, Jackson G, Jones PJ, Lambert JA (1998) A hierarchy of propositions: deciding which level to address in casework. Sci Justice 38(4):231–239 · DOI
  21. Evett IW, Jackson G, Lambert JA (2000) More on the hierarchy of propositions: exploring the distinction between explanations and propositions. Sci Justice 401(1):3–10 · DOI
  22. Champod C, Evett IW, Jackson G (2004) Establishing the most appropriate databases for addressing source level propositions. Sci Justice 44(3):153–164 · DOI
  23. Evett IW (1998) Towards a uniform framework for reporting opinions in forensic science casework. Sci Justice 38(3):198–202 · DOI
  24. Neumann C, Evett IW, Skerrett JE, Mateos-Garcia I (2011) Quantitative assessment of evidential weight for a fingerprint comparison I: generalisation to the comparison of a mark with set of ten prints from a suspect. Forensic Sci Int 207:101–105 · DOI
  25. Neumann C, Evett I, Skerret JE (2012) Quantifying the weight of evidence from a forensic fingerprint comparison: a new paradigm. J R Stat Soc Ser A: Stat Soc 175(2):371–415 · DOI
  26. Taroni F, Aitken CGG, Garbolino P (2001) De Finetti’s subjectivism, the assessment of probabilities and the evaluation of evidence: a commentary for forensic scientists. Sci Justice 41(3):145–150 · DOI
  27. Doddington G, Liggett W, Martin A, Przybocki M, Reynolds DA (1998) Sheeps, goats, lambs and wolves: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation. In: Proceedings of ICSLP · DOI
  28. Hepler AB, Saunders CP, Davis LJ, Buscaglia J (2011) Score-based likelihood ratios for handwriting evidence. Foresic Sci Int 219(1–3):129–140
  29. Meuwly D (2001) Reconaissance de Locuteurs en Sciences Forensiques: L’apport d’une Approache Automatique. PhD thesis, IPSC-Universite de Lausanne
  30. Navratil J, Ramaswamy G (2003) The awe and mystery of T-Norm. In: Proceedings of ESCA European conference on speech, communication and technology, EuroSpeech, pp 2009–2012 · DOI
  31. Gonzalez-Rodriguez J, Fierrez-Aguilar J, Ramos-Castro D, Ortega-Garcia J (2005) Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems. Forensic Sci Int 155(2–3):126–140 · DOI
  32. Pigeon S, Druyts P, Verlinde P (2000) Applying logistic regression to the fusion of the NIST’99 1-speaker submissions. Digit Signal Process 10(1):237–248 · DOI
  33. Brümmer N, Burget L, Cernocky J, Glembek O, Grezl F, Karafiat M, van Leeuwen DA, Matejka P, Scwartz P, Strasheim A (2007) Fusion of heterogeneous speaker recognition systems in the STBU submission for the NIST speaker recognition evaluation 2006. IEEE Trans Audio Speech Signal Process 15(7):2072–2084 · DOI
  34. Gonzalez-Rodriguez J, Rose P, Ramos D, Toledano DT, Ortega-Garcia J (2007) Emulating DNA: rigorous quantification of evidential weight in transparent and testable forensic speaker recognition. IEEE Trans Audio Speech Signal Process 15(7):2072–2084 · DOI
  35. Vergeer P, Bolck A, Peschier LJ, Berger CE, Hendriks JN (2014) Likelihood ratio methods for forensic comparison of evaporated gasoline residues. Sci Justice 56(6):401–411 · DOI
  36. Morrison GS (2009) Likelihood-ratio-based forensic speaker comparison using parametric representations of vowel formant trajectories. J Acoust Soc Am 125:2387–2397 · DOI
  37. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley
  38. Cappelli R, Ferrara M, Maltoni D (2010) Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans Pattern Anal Mach Intell 32:2128–2141 · DOI
  39. Cappelli R, Ferrara M, Maltoni D (2010) Fingerprint indexing based on minutia cylinder code. IEEE Trans Pattern Anal Mach Intell 33:1051–1057 · DOI
  40. Ferrara M, Maltoni D, Cappelli R (2012) Noninvertible minutia cylinder-code representation. IEEE Trans Inf Forensics Secur 7:1727–1737 · DOI
  41. Egli N (2009) Interpretation of partial fingermarks using an automated fingerprint identification system. PhD thesis, Institute de Police Scientifique, Ecole de Sciences Criminelles
  42. Auckenthaler R, Carey M, Lloyd-Tomas H (2000) Score normalization for text-independent speaker verification systems. Digit Signal Process 10:42–54 · DOI

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Metadata

Authors
Ramos, D., Krish, R. P., Fierrez, J., & Meuwly, D.
Venue
Advances in Computer Vision and Pattern Recognition
Published
2017
Pages
305-327
ISSN
  • 2191-6586
  • 2191-6594
Cited by
18
References
0

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