Human age identification via machine learning

Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/3002

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DC Field	Value	Language
dc.contributor.author	Naleer, H.M.M.
dc.date.accessioned	2018-02-01T05:10:08Z
dc.date.available	2018-02-01T05:10:08Z
dc.date.issued	2017-12-07
dc.identifier.citation	7th International Symposium 2017 on “Multidisciplinary Research for Sustainable Development”. 7th - 8th December, 2017. South Eastern University of Sri Lanka, University Park, Oluvil, Sri Lanka. pp. 154-158.	en_US
dc.identifier.isbn	978-955-627-120-1
dc.identifier.uri	http://ir.lib.seu.ac.lk/handle/123456789/3002
dc.description.abstract	A crucial point in Human Age Identification via Machine Learning is basically about automated systems learning to classify patterns and interactions in digital data sets. To achieve our objective, the paper is indicated a face model for appearing at low, middle and high resolution respectively. On age estimation, The Group Sparse Representation Based on Robust Regression (GSRBRR) formulation for mapping feature vectors to its age label. The different kind of regression methods are used to justified the testing results. Keywords: Sparse Representation, Low Resolution, High Resolution, Face Features	en_US
dc.language.iso	en_US	en_US
dc.publisher	South Eastern University of Sri Lanka, University Park, Oluvil, Sri Lanka	en_US
dc.subject	Sparse representation	en_US
dc.subject	Low resolution	en_US
dc.subject	High resolution	en_US
dc.subject	Face features	en_US
dc.title	Human age identification via machine learning	en_US
dc.type	Article	en_US
Appears in Collections:	7th International Symposium - 2017

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