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dc.contributor.authorBukar, Ali M.*
dc.contributor.authorUgail, Hassan*
dc.contributor.authorConnah, David*
dc.date.accessioned2016-08-08T08:48:20Z
dc.date.available2016-08-08T08:48:20Z
dc.date.issued2016-08-01
dc.identifier.citationBukar AM, Ugail H and Connah D (2016) Automatic age and gender classification using supervised appearance model. Journal of Electronic Imaging. 25(6): 061605.en_US
dc.identifier.urihttp://hdl.handle.net/10454/8760
dc.descriptionYesen_US
dc.description.abstractAge and gender classification are two important problems that recently gained popularity in the research community, due to their wide range of applications. Research has shown that both age and gender information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical model that captures shape and texture variations, has been one of the most widely used feature extraction techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when used for classification. This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the problems of age and gender classification. Our experiments show that sAM has better predictive power than the conventional AAM.en_US
dc.language.isoenen_US
dc.rights© 2016 The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.en_US
dc.subjectSupervised appearance model; Age estimation; Gender classification; Partial least-squares regression; Regressionen_US
dc.titleAutomatic age and gender classification using supervised appearance modelen_US
dc.status.refereedYesen_US
dc.date.Accepted2016-07-14
dc.date.application2016-08-01
dc.typeArticleen_US
dc.type.versionpublished version paperen_US
dc.identifier.doihttps://doi.org/10.1117/1.JEI.25.6.061605
refterms.dateFOA2018-07-25T13:31:04Z


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