Show simple item record

dc.contributor.convenorJan-Olof Eklundh, Yuichi Ohta, Steven Tanimoto
dc.contributor.authorBrown, Douglas
dc.contributor.authorLi, Hanxi
dc.contributor.authorGao, Yongsheng
dc.contributor.editorIAPR
dc.date.accessioned2017-05-03T14:13:06Z
dc.date.available2017-05-03T14:13:06Z
dc.date.issued2012
dc.date.modified2013-08-22T23:11:49Z
dc.identifier.isbn978-1-4673-2216-4
dc.identifier.issn1051-4651
dc.identifier.refurihttp://www.icpr2012.org/index.html
dc.identifier.urihttp://hdl.handle.net/10072/52305
dc.description.abstractLinear Regression Classification (LRC) based face recognition achieves high accuracy while being highly efficient. As with most other linear-subspace-based methods, the faces of a subject are assumed to reside on a linear manifold; however, where occlusion or disturbances are involved, this assumption may be inaccurate. In this paper, a manifold-learning procedure is used to expand on conventional LRC by excluding faces not fitting the original assumption (of linearity), thereby localizing the manifold subspace, increasing the accuracy over conventional LRC and reducing the number of faces for which the regression must be performed. The algorithm is evaluated using two standard databases and shown to outperform the conventional LRC.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent637572 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherInternational Association for Pattern Recognition
dc.publisher.placeJapan
dc.publisher.urihttp://ieeexplore.ieee.org/document/6460448
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencename21st International Conference on Pattern Recognition (ICPR)
dc.relation.ispartofconferencetitle2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012)
dc.relation.ispartofdatefrom2012-11-11
dc.relation.ispartofdateto2012-11-15
dc.relation.ispartoflocationUniv Tsukuba, Tsukuba, JAPAN
dc.relation.ispartofpagefrom1586
dc.relation.ispartofpageto1589
dc.rights.retentionY
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode080109
dc.titleLocality-Regularized Linear Regression for Face Recognition
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.date.issued2012
gro.hasfulltextFull Text
gro.griffith.authorBrown, Douglas L.
gro.griffith.authorGao, Yongsheng
gro.griffith.authorLi, Hanxi


Files in 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