The use of the likelihood ratio for evaluative and investigative purposes in comparative forensic handwriting examination

Forensic Sci Int. 2012 Jan 10;214(1-3):189-94. doi: 10.1016/j.forsciint.2011.08.007. Epub 2011 Sep 9.

Abstract

This paper extends previous research and discussion on the use of multivariate continuous data, which are about to become more prevalent in forensic science. As an illustrative example, attention is drawn here on the area of comparative handwriting examinations. Multivariate continuous data can be obtained in this field by analysing the contour shape of loop characters through Fourier analysis. This methodology, based on existing research in this area, allows one describe in detail the morphology of character contours throughout a set of variables. This paper uses data collected from female and male writers to conduct a comparative analysis of likelihood ratio based evidence assessment procedures in both, evaluative and investigative proceedings. While the use of likelihood ratios in the former situation is now rather well established (typically, in order to discriminate between propositions of authorship of a given individual versus another, unknown individual), focus on the investigative setting still remains rather beyond considerations in practice. This paper seeks to highlight that investigative settings, too, can represent an area of application for which the likelihood ratio can offer a logical support. As an example, the inference of gender of the writer of an incriminated handwritten text is forwarded, analysed and discussed in this paper. The more general viewpoint according to which likelihood ratio analyses can be helpful for investigative proceedings is supported here through various simulations. These offer a characterisation of the robustness of the proposed likelihood ratio methodology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Female
  • Forensic Sciences / methods
  • Handwriting*
  • Humans
  • Likelihood Functions*
  • Male
  • Sex Factors