View-independent person identification from human gait

Zonghua Zhang, Nikolaus F. Troje

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

Based on a three-dimensional (3D) linear model and the Bayesian rule, a method is explored to identify human walkers from two-dimensional (2D) motion sequences taken from different viewpoints. Principal component analysis constructs the 3D linear model from a set of Fourier represented examples. The sets of coefficients derived from projecting 2D motion sequences onto the 3D model by means of a maximum a posterior estimate is used as a signature of a walker. Simulating an identification experiment on a set of walking data we show that these signatures show invariance across viewpoints and can be used for viewpoint-independent person identification. © 2005 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)250-256
Number of pages7
JournalNeurocomputing
Volume69
Issue number1-3
DOIs
Publication statusPublished - Dec 2005

Keywords

  • Linear model
  • Principal component analysis (PCA)
  • View-independent gait identification

Fingerprint Dive into the research topics of 'View-independent person identification from human gait'. Together they form a unique fingerprint.

  • Cite this