Gait as a Biometric for Person Identification in Video Sequences
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Abstract
The term gait recognition is typically used to signify the
identification of individuals in image sequences `by the way they
walk'. There is an increased interest in gait as a biometric,
mainly due to its non-intrusive as well as non-concealable nature.
Considerable research efforts are being devoted in the computer
vision community to characterize and extract gait dynamics
automatically from video. The objective is to use gait as a filter
(or indicator) to effectively enhance the overall recognition
performance of a system that uses multiple modalities. In this
proposal, we describe two different gait recognition
methods; a non-parametric method that uses the self-similarity plot of a walking sequence as the input feature for
classification; and a parametric method that estimates the
spatiotemporal parameters of gait (the cadence and stride length)
and exploits their linear relationship as a cue for
identification. Finally, because carried loads are gait-altering,
we also present a motion-based method to detect whether a walking
person carries an object (load).