li-onviewinvariantgait-2018.pdf (1.45 MB)
On view-invariant gait recognition: a feature selection solution
journal contribution
posted on 2018-07-01, 00:00 authored by N Jia, V Sanchez, Chang-Tsun LiChang-Tsun LiThe 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 biometricsVolume
7Issue
4Pagination
287 - 295Publisher
Institution of Engineering and TechnologyLocation
Stevenage, Eng.Publisher DOI
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ISSN
2047-4938eISSN
2047-4946Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2018, IETUsage metrics
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Keywords
image recognitiongait analysisfeature extractionimage reconstructionScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Scienceview-invariant gait recognition problemoptimised gallery templatemultiview gallery templatesreconstructed featuresOU-ISIR large population datasetsfeature selection solutionViFSPERFORMANCEInformation SystemsArtificial Intelligence and Image ProcessingDistributed Computing
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