  This work offers a design of a video surveillance system based on a soft
biometric -- gait identification from MoCap data. The main focus is on two
substantial issues of the video surveillance scenario: (1) the walkers do not
cooperate in providing learning data to establish their identities and (2) the
data are often noisy or incomplete. We show that only a few examples of human
gait cycles are required to learn a projection of raw MoCap data onto a
low-dimensional sub-space where the identities are well separable. Latent
features learned by Maximum Margin Criterion (MMC) method discriminate better
than any collection of geometric features. The MMC method is also highly robust
to noisy data and works properly even with only a fraction of joints tracked.
The overall workflow of the design is directly applicable for a day-to-day
operation based on the available MoCap technology and algorithms for gait
analysis. In the concept we introduce, a walker's identity is represented by a
cluster of gait data collected at their incidents within the surveillance
system: They are how they walk.
