Human identity and gender recognition from gait sequences with arbitrary walking directions

Jiwen Lu, Gang Wang, Pierre Moulin

Research output: Contribution to journalArticlepeer-review

Abstract

We investigate the problem of human identity and gender recognition from gait sequences with arbitrary walking directions. Most current approaches make the unrealistic assumption that persons walk along a fixed direction or a pre-defined path. Given a gait sequence collected from arbitrary walking directions, we first obtain human silhouettes by background subtraction and cluster them into several clusters. For each cluster, we compute the cluster-based averaged gait image as features. Then, we propose a sparse reconstruction based metric learning method to learn a distance metric to minimize the intra-class sparse reconstruction errors and maximize the inter-class sparse reconstruction errors simultaneously, so that discriminative information can be exploited for recognition. The experimental results show the efficacy of our approach.

Original languageEnglish (US)
Article number6671367
Pages (from-to)51-61
Number of pages11
JournalIEEE Transactions on Information Forensics and Security
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2014

Keywords

  • Human gait analysis
  • gender recognition
  • identity recognition
  • metric learning
  • sparse reconstruction

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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