Computation of Likelihood Ratios in Fingerprint Identification for Configurations of Any Number of Minutiæ

Authors

  • Cédric Neumann M.Sc.,

    1. The Forensic Science Service, Trident Court, 2920 Solihull Parkway, Birmingham Business Park, Birmingham B37 7YN, U.K.
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  • Christophe Champod Ph.D.,

    1. Ecole des Sciences Criminelles, Institut de Police Scientifique, Batochime, Quartier Sorge Université de Lausanne, CH-1015 Lausanne-Dorigny, Switzerland.
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  • Roberto Puch-Solis Ph.D.,

    1. The Forensic Science Service, Trident Court, 2920 Solihull Parkway, Birmingham Business Park, Birmingham B37 7YN, U.K.
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  • Nicole Egli M.Sc.,

    1. Ecole des Sciences Criminelles, Institut de Police Scientifique, Batochime, Quartier Sorge Université de Lausanne, CH-1015 Lausanne-Dorigny, Switzerland.
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  • Alexandre Anthonioz M.Sc.,

    1. Ecole des Sciences Criminelles, Institut de Police Scientifique, Batochime, Quartier Sorge Université de Lausanne, CH-1015 Lausanne-Dorigny, Switzerland.
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  • Andie Bromage-Griffiths B.Sc.

    1. The Forensic Science Service, Trident Court, 2920 Solihull Parkway, Birmingham Business Park, Birmingham B37 7YN, U.K.
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Additional information and reprint requests:
Cedric Neumann
The Forensic Science Service
2920 Trident Court
Solihull Parkway
Birmingham Business Park
Birmingham B377YN
U.K.
E-mail: cedric.neumann@mac.com

Abstract

ABSTRACT: Recent court challenges have highlighted the need for statistical research on fingerprint identification. This paper proposes a model for computing likelihood ratios (LRs) to assess the evidential value of comparisons with any number of minutiæ. The model considers minutiae type, direction and relative spatial relationships. It expands on previous work on three minutiae by adopting a spatial modeling using radial triangulation and a probabilistic distortion model for assessing the numerator of the LR. The model has been tested on a sample of 686 ulnar loops and 204 arches. Features vectors used for statistical analysis have been obtained following a preprocessing step based on Gabor filtering and image processing to extract minutiae data. The metric used to assess similarity between two feature vectors is based on an Euclidean distance measure. Tippett plots and rates of misleading evidence have been used as performance indicators of the model. The model has shown encouraging behavior with low rates of misleading evidence and a LR power of the model increasing significantly with the number of minutiæ. The LRs that it provides are highly indicative of identity of source on a significant proportion of cases, even when considering configurations with few minutiæ. In contrast with previous research, the model, in addition to minutia type and direction, incorporates spatial relationships of minutiæ without introducing probabilistic independence assumptions. The model also accounts for finger distortion.

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