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dc.contributor.authorElmahmudi, Ali A.M.
dc.contributor.authorUgail, Hassan
dc.date.accessioned2022-03-20T06:25:19Z
dc.date.accessioned2022-04-20T10:32:56Z
dc.date.available2022-03-20T06:25:19Z
dc.date.available2022-04-20T10:32:56Z
dc.date.issued2019-11
dc.identifier.citationElmahmudi AAM and Ugail H (2019) The Biharmonic Eigenface. Signal, Image and Video Processing. 13: 1639-1647.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18901
dc.descriptionYesen_US
dc.description.abstractPrincipal component analysis (PCA) is an elegant mechanism that reduces the dimensionality of a dataset to bring out patterns of interest in it. The preprocessing of facial images for efficient face recognition is considered to be one of the epitomes among PCA applications. In this paper, we introduce a novel modification to the method of PCA whereby we propose to utilise the inherent averaging ability of the discrete Biharmonic operator as a preprocessing step. We refer to this mechanism as the BiPCA. Interestingly, by applying the Biharmonic operator to images, we can generate new images of reduced size while keeping the inherent features in them intact. The resulting images of lower dimensionality can significantly reduce the computational complexities while preserving the features of interest. Here, we have chosen the standard face recognition as an example to demonstrate the capacity of our proposed BiPCA method. Experiments were carried out on three publicly available datasets, namely the ORL, Face95 and Face96. The results we have obtained demonstrate that the BiPCA outperforms the traditional PCA. In fact, our experiments do suggest that, when it comes to face recognition, the BiPCA method has at least 25% improvement in the average percentage error rate.en_US
dc.language.isoenen_US
dc.rights(c) 2019 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/)en_US
dc.subjectBiharmonic functionen_US
dc.subjectEigenfacesen_US
dc.subjectFace processingen_US
dc.subjectFace recognitionen_US
dc.subjectPrincipal content analysisen_US
dc.titleThe Biharmonic Eigenfaceen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-05-31
dc.date.application2019-06-07
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.identifier.doihttps://doi.org/10.1007/s11760-019-01514-4
dc.rights.licenseCC-BYen_US
dc.date.updated2022-03-20T06:25:21Z
refterms.dateFOA2022-04-20T10:33:11Z
dc.openaccess.statusopenAccessen_US


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