A Grassmann graph embedding framework for Gait analysis

Tee Connie, Michael Kah Ong Goh, Andrew Beng Jin Teoh

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Gait recognition is important in a wide range of monitoring and surveillance applications. Gait information has often been used as evidence when other biometrics is indiscernible in the surveillance footage. Building on recent advances of the subspace-based approaches, we consider the problem of gait recognition on the Grassmann manifold. We show that by embedding the manifold into reproducing kernel Hilbert space and applying the mechanics of graph embedding on such manifold, significant performance improvement can be obtained. In this work, the gait recognition problem is studied in a unified way applicable for both supervised and unsupervised configurations. Sparse representation is further incorporated in the learning mechanism to adaptively harness the local structure of the data. Experiments demonstrate that the proposed method can tolerate variations in appearance for gait identification effectively.

Original languageEnglish
Article number15
JournalEurasip Journal on Advances in Signal Processing
Volume2014
Issue number1
DOIs
Publication statusPublished - 2014 Feb

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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