This paper demonstrates gait recognition using only the trajectories of lower body joint angles projected into the walking plane. For this work, we begin with the position of 3 0 markers as projected into the sagittal or walking plane.We show a simple method for estimating the planar offsets between the markers and the underlying skeleton and joints; given these offsets we compute the joint angle trajectories, To compensate for systematic temporal variations from one instance to the next -predominantly distance and speed of walk -we Jir the number of footsteps and timenormalize the trajectories by a variance compensated time warping. We pe@orm recognition on two walking databases of 18 people (over 150 walk instances) using simple nearest neighbor algorithm with Euclidean distance as a measurement criteria. We also use the expected confusion metric as a means to estimate how well joint-angle signals will perform in a larger population.

Researchers in the gait community propose various features, either appearance or model based, which they believe encode certain individual traits. One of the main assumptions made in many gait recognition techniques is constant walking-speed. Even though the gait patterns are repeatable, changes in walking speed can influence the gait patterns themselves. In this work we explore how changes in walking speed affect gait parameters in terms of recognition performance. A speed-varying walking database was collected to allow us to investigate and quantify the impact of speed on gait recognition methodically. We develop a normalization procedure, which maps gait features across speeds, and demonstrate their utility in previously proposed appearance-based gait recognition methods.

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