Use of photogrammetry and biomechanical gait analysis to identify individuals

Photogrammetry and recognition of gait patterns are valuable tools to help identify perpetrators based on surveillance recordings. We have found that stature but only few other measures have a satisfying reproducibility for use in forensics. Several gait variables with high recognition rates were found. Especially the variables located in the frontal plane are interesting due to large inter-individual differences in time course patterns. The variables with high recognition rates seem preferable for use in forensic gait analysis and as input variables to waveform analysis techniques such as principal component analysis resulting in marginal scores, which are difficult to interpret individually. Finally, a new gait model is presented based on functional principal component analysis with potentials for detecting individual gait patterns where time course patterns can be marginally interpreted directly in terms of the input variables. In this presentation, the above methods will be discussed exemplified with forensic cases.