From Biometric Scores to Forensic Likelihood Ratios

In this chapter, we describe the issue of the interpretation of forensic evidence from scores computed by a biometric system. This is one of the most important topics into the so-called area of forensic biometrics. We will show the importance of the topic, introducing some of the key concepts of forensic science with respect to the interpretation of results prior to their presentation in court, which is increasingly addressed by the computation of likelihood ratios (LR). We will describe the LR methodology, and will illustrate it with an example of the evaluation of fingerprint evidence in forensic conditions, by means of a fingerprint biometric system.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  David A. van Leeuwen,et al.  An Introduction to Application-Independent Evaluation of Speaker Recognition Systems , 2007, Speaker Classification.

[3]  Roland Auckenthaler,et al.  Score Normalization for Text-Independent Speaker Verification Systems , 2000, Digit. Signal Process..

[4]  I. Evett,et al.  More on the hierarchy of propositions: exploring the distinction between explanations and propositions. , 2000, Science & justice : journal of the Forensic Science Society.

[5]  Amanda B. Hepler,et al.  Score-based likelihood ratios for handwriting evidence. , 2012, Forensic science international.

[6]  I. W. Evett,et al.  Towards a uniform framework for reporting opinions in forensic science casework , 1998 .

[7]  Didier Meuwly,et al.  Introducing a Semi‐Automatic Method to Simulate Large Numbers of Forensic Fingermarks for Research on Fingerprint Identification , 2012, Journal of forensic sciences.

[8]  M. Nicole,et al.  Interpretation of partial fingermarks using an automated fingerprint identification system , 2009 .

[9]  Grzegorz Zadora,et al.  Information‐Theoretical Assessment of the Performance of Likelihood Ratio Computation Methods , 2013, Journal of forensic sciences.

[10]  A Bolck,et al.  Likelihood ratio methods for forensic comparison of evaporated gasoline residues. , 2014, Science & justice : journal of the Forensic Science Society.

[11]  Grzegorz Zadora,et al.  Evaluation of glass samples for forensic purposes — An application of likelihood ratios and an information–theoretical approach , 2010 .

[12]  Cedric Neumann,et al.  Quantifying the weight of evidence from a forensic fingerprint comparison: a new paradigm , 2012 .

[13]  Niko Brümmer,et al.  Application-independent evaluation of speaker detection , 2006, Comput. Speech Lang..

[14]  David A. van Leeuwen,et al.  Fusion of Heterogeneous Speaker Recognition Systems in the STBU Submission for the NIST Speaker Recognition Evaluation 2006 , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[15]  Niko Brümmer,et al.  Towards Fully Bayesian Speaker Recognition: Integrating Out the Between-Speaker Covariance , 2011, INTERSPEECH.

[16]  J. Koehler,et al.  The Coming Paradigm Shift in Forensic Identification Science , 2005, Science.

[17]  Colin Aitken,et al.  Bayesian Networks and Probabilistic Inference in Forensic Science , 2006 .

[18]  Didier Meuwly,et al.  A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation. , 2017, Forensic science international.

[19]  C Champod,et al.  Establishing the most appropriate databases for addressing source level propositions. , 2004, Science & justice : journal of the Forensic Science Society.

[20]  Davide Maltoni,et al.  Fingerprint Indexing Based on Minutia Cylinder-Code , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Franco Taroni,et al.  Statistics and the Evaluation of Evidence for Forensic Scientists , 2004 .

[22]  Davide Maltoni,et al.  Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Daniel Ramos Forensic evaluation of the evidence using automatic speaker recognition systems , 2014 .

[24]  J A Lambert,et al.  A model for case assessment and interpretation. , 1998, Science & justice : journal of the Forensic Science Society.

[25]  Joaquin Gonzalez-Rodriguez,et al.  Reliable support: Measuring calibration of likelihood ratios. , 2013, Forensic science international.

[26]  C. Aitken,et al.  Expressing evaluative opinions: a position statement , 2011 .

[27]  Jirí Navrátil,et al.  The awe and mystery of t-norm , 2003, INTERSPEECH.

[28]  C. Aitken,et al.  De Finetti's subjectivism, the assessment of probabilities and the evaluation of evidence: a commentary for forensic scientists , 2001 .

[29]  C. Aitken,et al.  Statistics and the Evaluation of Evidence for Forensic Scientists: Aitken/Statistics and the Evaluation of Evidence for Forensic Scientists , 2005 .

[30]  Cedric Neumann,et al.  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. , 2011, Forensic science international.

[31]  Pascal Druyts,et al.  Applying Logistic Regression to the Fusion of the NIST'99 1-Speaker Submissions , 2000, Digit. Signal Process..

[32]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[33]  Agnieszka Martyna,et al.  Statistical Analysis in Forensic Science: Evidential Value of Multivariate Physicochemical Data , 2014 .

[34]  Umar Mohammed,et al.  Probabilistic Models for Inference about Identity , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  I. Evett,et al.  A hierarchy of propositions: deciding which level to address in casework , 1998 .

[36]  Davide Maltoni,et al.  Noninvertible Minutia Cylinder-Code Representation , 2012, IEEE Transactions on Information Forensics and Security.

[37]  Didier Meuwly,et al.  Measuring coherence of computer-assisted likelihood ratio methods. , 2015, Forensic science international.

[38]  G. Morrison Likelihood-ratio forensic voice comparison using parametric representations of the formant trajectories of diphthongs. , 2009, The Journal of the Acoustical Society of America.

[39]  Javier Ortega-Garcia,et al.  Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems. , 2005, Forensic science international.

[40]  Doroteo Torre Toledano,et al.  Emulating DNA: Rigorous Quantification of Evidential Weight in Transparent and Testable Forensic Speaker Recognition , 2007, IEEE Transactions on Audio, Speech, and Language Processing.