Deakin University
Browse
li-onviewinvariantgait-2018.pdf (1.45 MB)

On view-invariant gait recognition: a feature selection solution

Download (1.45 MB)
journal contribution
posted on 2018-07-01, 00:00 authored by N Jia, V Sanchez, Chang-Tsun LiChang-Tsun Li
The authors present an improved feature selection solution for the view-invariant gait recognition problem, based on their previously proposed method called view-invariant feature selector (ViFS), which automatically reconstruct an optimised gallery template from a set of multi-view gallery templates. They improved ViFS by introducing a constraint to make sure that the reconstructed features have the same scale as the original features, thus reducing the number of misclassifications caused by data misalignment. They evaluate the improved ViFS on the CASIA B and OU-ISIR large population datasets by performing a wide range of comparative studies in order to explore and confirm its effectiveness. Evaluation results indicate that the proposed framework is very effective for view-invariant gait recognition tasks.

History

Journal

IET biometrics

Volume

7

Pagination

287-295

Location

Stevenage, Eng.

Open access

  • Yes

ISSN

2047-4938

eISSN

2047-4946

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2018, IET

Issue

4

Publisher

Institution of Engineering and Technology