Employing domain specific discriminative information to address inherent limitations of the LBP descriptor in face recognition

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Abstract

The 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.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks (IJCNN)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
Volume2018-July
ISBN (Electronic)9781509060146
DOIs
Publication statusPublished - 15 Oct 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018
http://www.ecomp.poli.br/~wcci2018/

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
Abbreviated titleIJCNN
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18
Internet address

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