Dynamic distance-based shape features for gait recognition

Tenika Whytock, Alexander Belyaev, Neil Robertson

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)
269 Downloads (Pure)


We propose a novel skeleton-based approach
to gait recognition. The core of our method consists of
employing the screened Poisson equation for construct-
ing a family of smooth distance functions associated
with a given shape. The screened Poisson distance func-
tion approximations nicely absorb shape boundary per-
turbations and allow us to dene a rough shape skele-
ton which is relatively stable to such boundary per-
turbations. We demonstrate that pixel-wise variance of
silhouette skeletons is a powerful gait cycle descriptor
leading to a signicant improvement over the existing
state of the art gait recognition rate.
Original languageEnglish
Pages (from-to)314–326
JournalJournal of Mathematical Imaging and Vision
Early online date4 Mar 2014
Publication statusPublished - Nov 2014


  • Gait recognition


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