Abstract
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.
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 language | English |
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Pages (from-to) | 314–326 |
Journal | Journal of Mathematical Imaging and Vision |
Volume | 50 |
Early online date | 4 Mar 2014 |
DOIs | |
Publication status | Published - Nov 2014 |
Keywords
- Gait recognition
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Alexander Belyaev
- School of Engineering & Physical Sciences - Associate Professor
- School of Engineering & Physical Sciences, Institute of Sensors, Signals & Systems - Associate Professor
Person: Academic (Research & Teaching)