Forensic gait analysis — Morphometric assessment from surveillance footage

Dilan Seckiner, Xanthé Mallett, Philip Maynard*, Didier Meuwly, Claude Roux

*Corresponding author for this work

    Research output: Contribution to journalReview articleAcademicpeer-review

    9 Citations (Scopus)
    218 Downloads (Pure)


    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.

    Original languageEnglish
    Pages (from-to)57-66
    Number of pages10
    JournalForensic science international
    Publication statusPublished - Mar 2019


    • Gait analysis
    • Morphometric assessment
    • Surveillance footage


    Dive into the research topics of 'Forensic gait analysis — Morphometric assessment from surveillance footage'. Together they form a unique fingerprint.

    Cite this