Show simple item record

dc.contributor.authorJia, N
dc.contributor.authorLi, CT
dc.contributor.authorSanchez, V
dc.contributor.authorLiew, AWC
dc.date.accessioned2018-03-28T01:40:46Z
dc.date.available2018-03-28T01:40:46Z
dc.date.issued2017
dc.identifier.isbn9781509057917
dc.identifier.doi10.1109/IWBF.2017.7935092
dc.identifier.urihttp://hdl.handle.net/10072/372664
dc.description.abstractView-invariant gait recognition is one of the major challenges in identifying people through their gait. Many researchers have evaluated view angle transformation techniques, discriminant analysis and manifold learning approaches for cross-view recognition, and their proposals are usually based on a common factor, i.e., to establish a cross-view mapping between gallery and probe templates. However, their effectiveness is restricted to small view angle variances. A promising approach to perform view-invariant gait recognition is through multi-view feature learning. In this paper, we propose the view-invariant feature selector (ViFS) and integrate it in a framework for view-invariant gait recognition. ViFS select features from multi-view gait templates and reconstructs gallery templates that accurately match the data for a specific view angle. ViFS is thus able to reconstruct gallery templates from arbitrary view angles, and thus help to transfer the cross-view problem to identical-view gait recognition. We also apply linear subspace learning methods as feature enhancers for ViFS, which substantially reduce the computational cost and improve the recognition speed. We test the proposed framework on the CASIA Dataset B. The average recognition accuracy of the proposed framework for 11 different views exceed 98%.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameIWBF 2017
dc.relation.ispartofconferencetitleProceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017
dc.relation.ispartofdatefrom2017-04-04
dc.relation.ispartofdateto2017-04-05
dc.relation.ispartoflocationCoventry, United Kingdom
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleFast and robust framework for view-invariant gait recognition
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record