Use of Photogrammetry and Biomechanical Gait analysis to Identify Individuals
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Use of Photogrammetry and Biomechanical Gait analysis to Identify Individuals. / Larsen, Peter Kastmand; Simonsen, Erik Bruun; Lynnerup, Niels.
2010. Abstract from 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, Denmark.Research output: Contribution to conference › Conference abstract for conference › Research
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TY - ABST
T1 - Use of Photogrammetry and Biomechanical Gait analysis to Identify Individuals
AU - Larsen, Peter Kastmand
AU - Simonsen, Erik Bruun
AU - Lynnerup, Niels
PY - 2010
Y1 - 2010
N2 - Photogrammetry and recognition of gait patterns are valuabletools to help identify perpetrators based on surveillancerecordings.We have found that stature but only few other measures havea satisfying reproducibility for use in forensics.Several gait variables with high recognition rates werefound. Especially the variables located in the frontal planeare interesting due to large inter-individual differences intime course patterns.The variables with high recognition rates seem preferablefor use in forensic gait analysis and as input variables towaveform analysis techniques such as principal componentanalysis resulting in marginal scores, which are difficult tointerpret individually.Finally, a new gait model is presented based on functionalprincipal component analysis with potentials for detectingindividual gait patterns where time course patterns can bemarginally interpreted directly in terms of the input variables.In this presentation, the above methods will be discussedexemplified with forensic cases.
AB - Photogrammetry and recognition of gait patterns are valuabletools to help identify perpetrators based on surveillancerecordings.We have found that stature but only few other measures havea satisfying reproducibility for use in forensics.Several gait variables with high recognition rates werefound. Especially the variables located in the frontal planeare interesting due to large inter-individual differences intime course patterns.The variables with high recognition rates seem preferablefor use in forensic gait analysis and as input variables towaveform analysis techniques such as principal componentanalysis resulting in marginal scores, which are difficult tointerpret individually.Finally, a new gait model is presented based on functionalprincipal component analysis with potentials for detectingindividual gait patterns where time course patterns can bemarginally interpreted directly in terms of the input variables.In this presentation, the above methods will be discussedexemplified with forensic cases.
M3 - Conference abstract for conference
Y2 - 23 August 2010 through 27 August 2010
ER -
ID: 33902068