Forensic gait analysis - Morphometric assessment from surveillance footage.


Journal

Forensic science international
ISSN: 1872-6283
Titre abrégé: Forensic Sci Int
Pays: Ireland
ID NLM: 7902034

Informations de publication

Date de publication:
Mar 2019
Historique:
received: 22 11 2018
accepted: 11 01 2019
pubmed: 29 1 2019
medline: 15 3 2019
entrez: 29 1 2019
Statut: ppublish

Résumé

Following the technological rise of surveillance cameras and their subsequent proliferation in public places, the use of information gathered by such means for investigative and evaluative purposes sparked a large interest in the forensic community and within policing scenarios. In particular, it is suggested that analysis of the body, especially the assessment of gait characteristics, can provide useful information to aid the investigation. This paper discusses the influences upon gait to mitigate some of the limitations of surveillance footage, including those due to the varying anatomical differences between individuals. Furthermore, the differences between various techniques applied to assess gait are discussed, including biometric gait recognition, forensic gait analysis, tracking technology, and marker technology. This review article discusses the limitations of the current methods for assessment of gait; exposing gaps within the literature in regard to various influences impacting upon the gait cycle. Furthermore, it suggests a 'morphometric' technique to enhance the available procedures to potentially facilitate the development of standardised protocols with supporting statistics and database. This in turn will provide meaningful information to forensic investigation, intelligence-gathering processes, and potentially as an additional method of forensic evaluation of evidence.

Identifiants

pubmed: 30690252
pii: S0379-0738(19)30016-7
doi: 10.1016/j.forsciint.2019.01.007
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

57-66

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Auteurs

Dilan Seckiner (D)

Centre for Forensic Science, University of Technology Sydney, 15 Broadway, Ultimo, New South Wales, 2007, Australia.

Xanthé Mallett (X)

School of Humanities and Social Science, University of Newcastle, Callaghan, New South Wales, 2308, Australia.

Philip Maynard (P)

Centre for Forensic Science, University of Technology Sydney, 15 Broadway, Ultimo, New South Wales, 2007, Australia. Electronic address: Philip.Maynard@uts.edu.au.

Didier Meuwly (D)

Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, The Netherlands; University of Twente, Enschede, The Netherlands.

Claude Roux (C)

Centre for Forensic Science, University of Technology Sydney, 15 Broadway, Ultimo, New South Wales, 2007, Australia.

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Classifications MeSH