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dc.contributor.authorBukar, Ali M.*
dc.contributor.authorUgail, Hassan*
dc.date.accessioned2017-06-14T08:50:22Z
dc.date.available2017-06-14T08:50:22Z
dc.date.issued2018
dc.identifier.citationBukar AM and Ugail H (2018) A nonlinear appearance model for age progression. In: Hassanien AE and Oliva DA (Eds.) Advances in soft computing and machine learning in image processing. London: Springer.en_US
dc.identifier.urihttp://hdl.handle.net/10454/12200
dc.descriptionNoen_US
dc.description.abstractRecently, automatic age progression has gained popularity due to its nu-merous applications. Among these is the search for missing people, in the UK alone up to 300,000 people are reported missing every year. Although many algorithms have been proposed, most of the methods are affected by image noise, illumination variations, and most importantly facial expres-sions. To this end we propose to build an age progression framework that utilizes image de-noising and expression normalizing capabilities of kernel principal component analysis (Kernel PCA). Here, Kernel PCA a nonlinear form of PCA that explores higher order correlations between input varia-bles, is used to build a model that captures the shape and texture variations of the human face. The extracted facial features are then used to perform age progression via a regression procedure. To evaluate the performance of the framework, rigorous tests are conducted on the FGNET ageing data-base. Furthermore, the proposed algorithm is used to progress images of Mary Boyle; a six-year-old that went missing over 39 years ago, she is considered Ireland’s youngest missing person. The algorithm presented in this paper could potentially aid, among other applications, the search for missing people worldwide.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://www.springer.com/gp/book/9783319637532
dc.subjectAge progression; Age synthesis; Kernel appearance model; Linear regression, Kernel PCA; Kernel preimage, Mary Boyleen_US
dc.titleA nonlinear appearance model for age progressionen_US
dc.status.refereedYesen_US
dc.date.Accepted2017-04-10
dc.typeBook chapteren_US
dc.type.versionNo full-text in the repositoryen_US


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