Gait tracking and recognition using person-dependent dynamic shape model

Chan Su Lee, Ahmed Elgammal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationFGR 2006
Subtitle of host publicationProceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Pages553-559
Number of pages7
DOIs
StatePublished - 2006
EventFGR 2006: 7th International Conference on Automatic Face and Gesture Recognition - Southampton, United Kingdom
Duration: Apr 10 2006Apr 12 2006

Publication series

NameFGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Volume2006

Other

OtherFGR 2006: 7th International Conference on Automatic Face and Gesture Recognition
CountryUnited Kingdom
CitySouthampton
Period4/10/064/12/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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