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A new uniform framework of source attribution in forensic science

Author

Listed:
  • Zhihui Li

    (Ministry of Public Security)

  • Yao Liu

    (Ministry of Public Security)

  • Xiyuan Hu

    (Nanjing University of Science and Technology)

  • Guiqiang Wang

    (Ministry of Public Security)

Abstract

Scientific principles of forensic source identification have attracted widespread interest in recent years. Among those presented principles and theorems, the Bayes inference was regarded as one of the most scientific principles. In this paper, we argue that the Bayes theorem is in challenge when used as principal basis for forensic source identification. Furthermore, two novel concepts: feature-matching value and feature-matching identification value are proposed inspired by the basic ideas of information theory. Based on these two concepts, a new framework is established to describe the source identification principles of forensic science. The proposed source identification principle uses deduction logic structure, and unifies the three existing source identification paradigms. The newly proposed framework is expected to provide a solid scientific basis for the source attribution methods in forensic science.

Suggested Citation

  • Zhihui Li & Yao Liu & Xiyuan Hu & Guiqiang Wang, 2022. "A new uniform framework of source attribution in forensic science," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01277-3
    DOI: 10.1057/s41599-022-01277-3
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    References listed on IDEAS

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    1. Norman Fenton, 2011. "Improve statistics in court," Nature, Nature, vol. 479(7371), pages 36-37, November.
    2. Ronald J. Allen & Michael S. Pardo, 2007. "The Problematic Value of Mathematical Models of Evidence," The Journal of Legal Studies, University of Chicago Press, vol. 36(1), pages 107-140, January.
    3. C. Neumann & I. W. Evett & J. Skerrett, 2012. "Quantifying the weight of evidence from a forensic fingerprint comparison: a new paradigm," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 371-415, April.
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