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dc.contributor.authorFan, Junjie
dc.contributor.authorArandjelovic, Ognjen
dc.date.accessioned2019-01-08T15:30:05Z
dc.date.available2019-01-08T15:30:05Z
dc.date.issued2018-10-15
dc.identifier.citationFan , J & Arandjelovic , O 2018 , Employing domain specific discriminative information to address inherent limitations of the LBP descriptor in face recognition . in 2018 International Joint Conference on Neural Networks (IJCNN) . vol. 2018-July , 8489691 , Institute of Electrical and Electronics Engineers Inc. , 2018 International Joint Conference on Neural Networks, IJCNN 2018 , Rio de Janeiro , Brazil , 8/07/18 . https://doi.org/10.1109/IJCNN.2018.8489691en
dc.identifier.citationconferenceen
dc.identifier.isbn9781509060146
dc.identifier.otherPURE: 256751425
dc.identifier.otherPURE UUID: 56461afb-4acf-45ce-a13f-3444b8335729
dc.identifier.otherScopus: 85048817033
dc.identifier.otherWOS: 000585967403117
dc.identifier.urihttp://hdl.handle.net/10023/16799
dc.description.abstractThe local binary patern (LBP) descriptor and its derivatives have a demonstrated track record of good performance in face recognition. Nevertheless the original descriptor, the framework within which it is employed, and the aforementioned improvements of these in the existing literature, all suffer from a number of inherent limitations. In this work we highlight these and propose novel ways of addressing them in a principled fashion. Specifically, we introduce (i) gradient based weighting of local descriptor contributions to region based histograms as a means of avoiding data smoothing by non-discriminative image loci, and (ii) Gaussian fuzzy region membership as a means of achieving robustness to registration errors. Importantly, the nature of these contributions allows the proposed techniques to be combined with the existing extensions to the LBP descriptor thus making them universally recommendable. Effectiveness is demonstrated on the notoriously challenging Extended Yale B face corpus.
dc.format.extent7
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2018 International Joint Conference on Neural Networks (IJCNN)en
dc.rights© 2018, IEEE. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/IJCNN.2018.8489691en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQA76 Computer softwareen
dc.subjectT Technologyen
dc.subjectArtificial Intelligenceen
dc.subjectSoftwareen
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccQA76en
dc.subject.lccTen
dc.titleEmploying domain specific discriminative information to address inherent limitations of the LBP descriptor in face recognitionen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1109/IJCNN.2018.8489691


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