Please use this identifier to cite or link to this item: http://ir-library.mmust.ac.ke:8080/xmlui/handle/123456789/1714
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dc.contributor.authorAngulu, Raphael-
dc.contributor.authorTapamo, Jules R.-
dc.contributor.authorAdewumi, Aderemi O.-
dc.date.accessioned2021-06-22T13:49:34Z-
dc.date.available2021-06-22T13:49:34Z-
dc.date.issued2018-06-06-
dc.identifier.urihttps://doi.org/10.1186/s13640-018-0278-6-
dc.identifier.urihttps://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-018-0278-6-
dc.identifier.urihttp://r-library.mmust.ac.ke/123456789/1714-
dc.description.abstractFacial aging adversely impacts performance of face recognition and face verification and authentication using facial features. This stochastic personalized inevitable process poses dynamic theoretical and practical challenge to the computer vision and pattern recognition community. Age estimation is labeling a face image with exact real age or age group. How do humans recognize faces across ages? Do they learn the pattern or use age-invariant features? What are these age-invariant features that uniquely identify one across ages? These questions and others have attracted significant interest in the computer vision and pattern recognition research community. In this paper, we present a thorough analysis of recent research in aging and age estimation. We discuss popular algorithms used in age estimation, existing models, and how they compare with each other; we compare performance of various systems and how they are evaluated, age estimation challenges, and insights for future research.en_US
dc.language.isoenen_US
dc.publisherEURASIP Journal on Image and Video Processingen_US
dc.subjectAge, estimation, via, face, imagesen_US
dc.titleAge estimation via face images: a survey.en_US
dc.typeArticleen_US
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