Employing domain specific discriminative information to address inherent limitations of the LBP descriptor in face recognition
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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.
Fan , 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.2018.8489691conference
2018 International Joint Conference on Neural Networks (IJCNN)
© 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.2018.8489691
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