Gait recognition by two-stage principal component analysis

Sandhitsu R. Das, Robert C. Wilson, Maciej T. Lazarewicz, Leif H. Finkel

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

9 Scopus citations

Abstract

We describe a methodology for classification of gait (walk, run, jog, etc.) and recognition of individuals based on gait using two successive stages of principal component analysis (PCA) on kinematic data. In psychophysical studies we have found that observers are sensitive to specific "motion features" that characterize human gait. These spatiotemporal motion features closely correspond to the first few principal components (PC) of the kinematic data. The first few PCs provide a representation of an individual gait as trajectory along a low-dimensional manifold in PC space. A second stage of PCA captures variability in the shape of this manifold across individuals or gaits. This simple eigenspace based analysis is capable of accurate classification across subjects.

Original languageEnglish (US)
Title of host publicationFGR 2006
Subtitle of host publicationProceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Pages579-584
Number of pages6
DOIs
StatePublished - 2006
Externally publishedYes
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

Conference

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

Keywords

  • Gait recognition
  • Motion features
  • Principal component analysis

ASJC Scopus subject areas

  • Engineering(all)

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