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From Biometric Scores to Forensic Likelihood Ratios

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Handbook of Biometrics for Forensic Science

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

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.

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Notes

  1. 1.

    http://www.enfsi.eu/.

  2. 2.

    The background information about the case I will be eliminated from the notation for the sake of simplicity hereafter. It will be assumed that all the probabilities defined are conditioned to I.

  3. 3.

    Here we work at the source level, and therefore same-source scores refer to scores generated from two biometric specimens coming from the same source. They are what in biometric authentication terminology are called genuine scores.

  4. 4.

    Here we work at the source level, and therefore different-source scores refer to scores generated from two biometric specimens coming from different sources. They are what in biometric authentication terminology are called impostor scores.

  5. 5.

    Typical implementations used in biometrics include toolkits like FoCal or BOSARIS, which can be found in http://niko.brummer.googlepages.com.

  6. 6.

    We have used the implementation of this score computation system provided by the authors.

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Author information

Authors and Affiliations

  1. ATVS - Biometric Recognition Group, Escuela Politecnica Superior, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente 11, 28049, Madrid, Spain

    Daniel Ramos, Ram P. Krish & Julian Fierrez

  2. Netherlands Forensic Institute, Laan van Ypenburg 6, 2497GB, The Hague, The Netherlands

    Didier Meuwly

  3. University of Twente, Drienerlolaan 5, 7522NB, Enschede, The Netherlands

    Didier Meuwly

Authors
  1. Daniel Ramos
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  2. Ram P. Krish
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  3. Julian Fierrez
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  4. Didier Meuwly
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Corresponding author

Correspondence to Daniel Ramos .

Editor information

Editors and Affiliations

  1. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. University of Lausanne, Lausanne, Switzerland

    Christophe Champod

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Ramos, D., Krish, R.P., Fierrez, J., Meuwly, D. (2017). From Biometric Scores to Forensic Likelihood Ratios. In: Tistarelli, M., Champod, C. (eds) Handbook of Biometrics for Forensic Science. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-50673-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-50673-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50671-5

  • Online ISBN: 978-3-319-50673-9

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