Evaluation of distance‐based approaches for forensic comparison: Application to hand odor evidence. Issue 6 (3rd August 2021)
- Record Type:
- Journal Article
- Title:
- Evaluation of distance‐based approaches for forensic comparison: Application to hand odor evidence. Issue 6 (3rd August 2021)
- Main Title:
- Evaluation of distance‐based approaches for forensic comparison: Application to hand odor evidence
- Authors:
- Rivals, Isabelle
Sautier, Cédric
Cognon, Guillaume
Cuzuel, Vincent - Abstract:
- Abstract: The issue of distinguishing between the same‐source and different‐source hypotheses based on various types of traces is a generic problem in forensic science. This problem is often tackled with Bayesian approaches, which are able to provide a likelihood ratio that quantifies the relative strengths of evidence supporting each of the two competing hypotheses. Here, we focus on distance‐based approaches, whose robustness and specifically whose capacity to deal with high‐dimensional evidence are very different, and need to be evaluated and optimized. A unified framework for direct methods based on estimating the likelihoods of the distance between traces under each of the two competing hypotheses, and indirect methods using logistic regression to discriminate between same‐source and different‐source distance distributions, is presented. Whilst direct methods are more flexible, indirect methods are more robust and quite natural in machine learning. Moreover, indirect methods also enable the use of a vectorial distance, thus preventing the severe information loss suffered by scalar distance approaches. Direct and indirect methods are compared in terms of sensitivity, specificity, and robustness, with and without dimensionality reduction, with and without feature selection, on the example of hand odor profiles, a novel and challenging type of evidence in the field of forensics. Empirical evaluations on a large panel of 534 subjects and their 1690 odor traces show theAbstract: The issue of distinguishing between the same‐source and different‐source hypotheses based on various types of traces is a generic problem in forensic science. This problem is often tackled with Bayesian approaches, which are able to provide a likelihood ratio that quantifies the relative strengths of evidence supporting each of the two competing hypotheses. Here, we focus on distance‐based approaches, whose robustness and specifically whose capacity to deal with high‐dimensional evidence are very different, and need to be evaluated and optimized. A unified framework for direct methods based on estimating the likelihoods of the distance between traces under each of the two competing hypotheses, and indirect methods using logistic regression to discriminate between same‐source and different‐source distance distributions, is presented. Whilst direct methods are more flexible, indirect methods are more robust and quite natural in machine learning. Moreover, indirect methods also enable the use of a vectorial distance, thus preventing the severe information loss suffered by scalar distance approaches. Direct and indirect methods are compared in terms of sensitivity, specificity, and robustness, with and without dimensionality reduction, with and without feature selection, on the example of hand odor profiles, a novel and challenging type of evidence in the field of forensics. Empirical evaluations on a large panel of 534 subjects and their 1690 odor traces show the significant superiority of the indirect methods, especially without dimensionality reduction, be it with or without feature selection. … (more)
- Is Part Of:
- Journal of forensic sciences. Volume 66:Issue 6(2021)
- Journal:
- Journal of forensic sciences
- Issue:
- Volume 66:Issue 6(2021)
- Issue Display:
- Volume 66, Issue 6 (2021)
- Year:
- 2021
- Volume:
- 66
- Issue:
- 6
- Issue Sort Value:
- 2021-0066-0006-0000
- Page Start:
- 2208
- Page End:
- 2217
- Publication Date:
- 2021-08-03
- Subjects:
- Bayesian inference -- dissimilarity measure -- forensic science -- human hand odor -- likelihood ratio -- logistic regression
Medical jurisprudence -- Periodicals
Forensic sciences -- Periodicals
Forensic Medicine -- Periodicals
Gerechtelijke geneeskunde
Gerechtelijke chemie
Gerechtelijke psychiatrie
363.2505 - Journal URLs:
- http://catalog.hathitrust.org/api/volumes/oclc/1754597.html ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1556-4029 ↗
http://www.blackwell-synergy.com/loi/jfo ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/1556-4029.14818 ↗
- Languages:
- English
- ISSNs:
- 0022-1198
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4984.600000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 19938.xml