Algorithms for Gait-Based Human Identification from a Monocular Video Sequence
Algorithms for Gait-Based Human Identification from a Monocular Video Sequence
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Date
2003-11-12
Authors
Kale, Amit A
Advisor
Chellappa, Rama
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Abstract
Human gait is a spatio-temporal phenomenon that characterizes the motion
characteristics of an individual. It is possible
to detect and measure gait even in low-resolution video. This makes it
an attractive modality in surveillance applications, where it is often
difficult to get face or iris information at high enough resolution
for recognition applications. Psychophysical studies indicate that
humans have the capability for recognizing people from even
impoverished displays of gait, indicating the presence of identity
information. From early medical studies it appears
that there are twenty four different components to human gait, and that if all
the measurements are considered, gait is unique. It is interesting,
therefore, to study the utility of gait as a biometric. The goal of
this thesis is to investigate the information contained in the video
sequences of human gait and how to extract and exploit that
information in ways that facilitate human identification.
In our work, we present both deterministic and stochastic approaches for gait recognition.
Human identification using gait, similar 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 show the utility of a
simple feature viz. the width of the outer contour of the binarized
silhouette of the subject and its derivatives for gait recognition in
a dynamic time warping framework. By virtue of their deterministic
nature, template matching methods have limited noise resilience. A careful analysis of gait would
reveal that it has two important components. The first is a structural
component that captures the physical build of a person while the
second is the motion kinematics of the body during a gait cycle. We propose 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 stochastic nature of the HMM yields
better noise resilience than the template matching technique.
To recognize a person walking at a large distance, humans try to combine
information such as posture, arm/leg swing, hip/upper body sway or
some unique movements that are characteristic of that person. We demonstrate the same effect
through fusion of different dynamic and static gait features in both
determinisitic and stochastic frameworks. Most gait recognition algorithms rely on the availability of an exact side view
in the probe. However, it is not realistic to expect that this assumption
will be valid in most real-life scenarios. We present a view invariant
gait recognition algorithm which is based on synthesizing a side view
of a person from an arbitrary monocular view. The method is based on the planar approximation of a
person that is valid when human identification at a distance is
desired.