Show simple item record

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


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search SEUIR


Browse

My Account