Human identification based on gait paths

Gait paths are spatial trajectories of selected body points during person's walk. We have proposed and evaluated features extracted from gait paths for the task of person identification. We have used the following gait paths: skeleton root element, feet, hands and head. In our motion capture laboratory we have collected human gait database containing 353 different motions of 25 actors. We have proposed four approaches to extract features from motion clips: statistical, histogram, Fourier transform and timeline We have prepared motion filters to reduce the impact of the actor's location and actor's height on the gait path. We have applied supervised machine learning techniques to classify gaits described by the proposed feature sets. We have prepared scenarios of the features selections for every approach and iterated classification experiments. On the basis of obtained classifications results we have discovered most remarkable features for the identification task. We have achieved almost 97% identification accuracy for normalized paths.