We study the problem of analyzing and classifying human gait by
modeling it as a stochastic process on a shape space. We consider
gait as a evolution of human silhouettes as seen in video
sequences and focus on their shapes. More specifically, we define
a shape space of planar, closed curves, and model a human gait as
a stochastic process on this space. Due to the periodic nature of
human walk, this process is naturally constrained to be
cyclostationary, i.e. its mean path are assumed to be cyclic. We
compare two subjects using a metric that quantifies differences
between average gait cycles of each subject. This computation
utilizes several tools from differential geometry of the shape
space, including computation of geodesics, estimation of means of
observed shapes, interpolation between observed shapes, and
temporal registration of two gait cycles. Finally, we apply a
nearest-neighbor classifier, using the gait metric, to perform
human recognition and present results from an experiment involving
26 subjects.