Show simple item record

dc.contributor.authorSanderson, Conrad
dc.contributor.authorHarandi, Mehrtash T
dc.contributor.authorWong, Yongkang
dc.contributor.authorLovell, Brian C
dc.date.accessioned2021-01-12T22:54:24Z
dc.date.available2021-01-12T22:54:24Z
dc.date.issued2012
dc.identifier.isbn9781467324991
dc.identifier.doi10.1109/avss.2012.23
dc.identifier.urihttp://hdl.handle.net/10072/400957
dc.description.abstractIn contrast to comparing faces via single exemplars, matching sets of face images increases robustness and discrimination performance. Recent image set matching approaches typically measure similarities between subspaces or manifolds, while representing faces in a rigid and holistic manner. Such representations are easily affected by variations in terms of alignment, illumination, pose and expression. While local feature based representations are considerably more robust to such variations, they have received little attention within the image set matching area. We propose a novel image set matching technique, comprised of three aspects: (i) robust descriptors of face regions based on local features, partly inspired by the hierarchy in the human visual system, (ii) use of several subspace and exemplar metrics to compare corresponding face regions, (iii) jointly learning which regions are the most discriminative while finding the optimal mixing weights for combining metrics. Experiments on LFW, PIE and MOBIO face datasets show that the proposed algorithm obtains considerably better performance than several recent state of-the-art techniques, such as Local Principal Angle and the Kernel Affine Hull Method.
dc.publisherIEEE
dc.relation.ispartofconferencename2012 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
dc.relation.ispartofconferencetitle2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance
dc.relation.ispartofdatefrom2012-09-18
dc.relation.ispartofdateto2012-09-21
dc.relation.ispartoflocationBeijing, China
dc.titleCombined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
dc.typeConference output
dcterms.bibliographicCitationSanderson, C; Harandi, MT; Wong, Y; Lovell, BC, Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
dc.date.updated2021-01-12T22:52:24Z
dc.description.versionAccepted Manuscript (AM)
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.hasfulltextFull Text
gro.griffith.authorSanderson, Conrad


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