Gait Tracking and Recognition Using Person-Dependent Dynamic Shape Model

Abstract

The characteristics of the 2D shape deformation in human motion contain rich information for human identification 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.