Dynamic Distance-Based Shape Features for Gait Recognition

Tenika Whytock, Alexander Belyaev, Neil M. Robertson

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)
166 Downloads (Pure)


We propose a novel skeleton-based approach to gait recognition using our Skeleton Variance Image. The core of our approach consists of employing the screened Poisson equation to construct a family of smooth distance functions associated with a given shape. The screened Poisson distance function approximation nicely absorbs and is relatively stable to shape boundary perturbations which allows us to define a rough shape skeleton. We demonstrate how our Skeleton Variance Image is a powerful gait cycle descriptor leading to a significant improvement over the existing state of the art gait recognition rate.
Original languageEnglish
JournalJournal of Mathematical Imaging and Vision
Early online date04 Mar 2014
Publication statusPublished - Nov 2014


  • Gait recognition


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