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2006
IEEE

Gait Tracking and Recognition Using Person-Dependent Dynamic Shape Model

9 years 3 months ago
Gait Tracking and Recognition Using Person-Dependent Dynamic Shape Model
Characteristics of the 2D shape deformation in human motion contain rich information for human identiļ¬cation and pose estimation. In this paper, we introduce a framework for simultaneous gait tracking and recognition using person-dependent global shape deformation model. Person-dependent global shape deformations are modeled using a nonlinear generative model with kinematic manifold embedding and kernel mapping. The kinematic manifold is used as a common representation of body pose dynamics in different people in a low dimensional space. Shape style as well as geometric transformation and body pose are estimated within a Bayesian framework using the generative model of global shape deformation. Experimental results show person-dependent synthesis of global shape deformation, gait recognition from extracted silhouettes using style parameters, and simultaneous gait tracking and recognition from image edges.
Chan-Su Lee, Ahmed M. Elgammal
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where FGR
Authors Chan-Su Lee, Ahmed M. Elgammal
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