Forensic interpretation framework for body and gait analysis: feature extraction, frequency and distinctiveness

Seckiner, D; Mallett, X; Roux, C; Gittelson, S; Maynard, P; Meuwly, D

Forensic interpretation framework for body and gait analysis: feature extraction, frequency and distinctiveness

Seckiner, D Mallett, X Roux, C

Gittelson, S Maynard, P Meuwly, D

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Publisher:: Taylor and Francis Group
Publication Type:: Journal Article
Citation:: Australian Journal of Forensic Sciences, 2024
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Seckiner, D
dc.contributor.author	Mallett, X
dc.contributor.author	Roux, C https://orcid.org/0000-0003-3610-420X
dc.contributor.author	Gittelson, S
dc.contributor.author	Maynard, P
dc.contributor.author	Meuwly, D
dc.date.accessioned	2024-03-14T05:22:46Z
dc.date.available	2024-03-14T05:22:46Z
dc.date.issued	2024-01-01
dc.identifier.citation	Australian Journal of Forensic Sciences, 2024
dc.identifier.issn	0045-0618
dc.identifier.issn	1834-562X
dc.identifier.uri	http://hdl.handle.net/10453/176710
dc.description.abstract	Surveillance is ubiquitous in modern society, allowing continuous monitoring of areas that results in capturing criminal (or suspicious) activity as footage. This type of trace is usually examined, assessed and evaluated by a forensic examiner to ultimately help the court make inferences about who was on the footage. The purpose of this study was to develop an analytical model that ensures applicability of morphometric (both anthropometric and morphological) techniques for photo-comparative analyses of body and gait of individuals in CCTV images, and then to assign a likelihood ratio. This is the first paper of a series: This paper will contain feature extraction to observe repeatability procedures from a single observer, in turn, producing the frequency and distinctiveness of the feature set within the given population. To achieve this, an Australian population database of 383 subjects (stance) and 268 subjects (gait) from both sexes, all ages above 18 and ancestries was generated. Features were extracted, defined, and their rarity viewed among the developed database. Repeatability studies were completed in which stance and gait (static and dynamic) features contained low levels of repeatability error (0.2%–1.5 TEM%). For morphological examination, finger flexion and feet placement were observed to have high observer performance.
dc.language	English
dc.publisher	Taylor and Francis Group
dc.relation.ispartof	Australian Journal of Forensic Sciences
dc.relation.isbasedon	10.1080/00450618.2022.2161636
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	“This is an Accepted Manuscript of an article published by Taylor & Francis in Australian Journal of Forensic Sciences on 2 January 2023, available online: https://doi.org/10.1080/00450618.2022.2161636”
dc.subject.classification	Legal & Forensic Medicine
dc.title	Forensic interpretation framework for body and gait analysis: feature extraction, frequency and distinctiveness
dc.type	Journal Article
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2024-03-14T05:22:45Z
pubs.publication-status	Published

Abstract:

Surveillance is ubiquitous in modern society, allowing continuous monitoring of areas that results in capturing criminal (or suspicious) activity as footage. This type of trace is usually examined, assessed and evaluated by a forensic examiner to ultimately help the court make inferences about who was on the footage. The purpose of this study was to develop an analytical model that ensures applicability of morphometric (both anthropometric and morphological) techniques for photo-comparative analyses of body and gait of individuals in CCTV images, and then to assign a likelihood ratio. This is the first paper of a series: This paper will contain feature extraction to observe repeatability procedures from a single observer, in turn, producing the frequency and distinctiveness of the feature set within the given population. To achieve this, an Australian population database of 383 subjects (stance) and 268 subjects (gait) from both sexes, all ages above 18 and ancestries was generated. Features were extracted, defined, and their rarity viewed among the developed database. Repeatability studies were completed in which stance and gait (static and dynamic) features contained low levels of repeatability error (0.2%–1.5 TEM%). For morphological examination, finger flexion and feet placement were observed to have high observer performance.

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http://hdl.handle.net/10453/176710