| Abstract | Human gait is a spatio-temporal phenomenon that characterizes the motion char-acteristics of an individual. It is possible to detect and measure gait even in low-
resolution video. In this chapter, we discuss algorithms for identifying people by
their gait from a monocular video sequence. Human identification using gait, sim-
ilar to text-based speaker identification, involves different individuals performing
the same task and a template-matching approach is suitable for such problems.
In situations where the amount of training data is limited, we demonstrate the
utility of a simple width feature for gait recognition. By virtue of their determin-
istic nature, template matching methods have limited noise resilience. In order
to deal with noise we introduce a systematic approach to gait recognition by
building representations for the structural and dynamic components of gait using
exemplars and hidden Markov models (HMMs). The above methods assume that
an exact side-view of the subject is available in the probe sequence. For the case
when the person walks at an arbitrary angle far away from the camera we present
a view invariant gait recognition algorithm which is based on synthesizing a side
view of a person from an arbitrary monocular view.
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