Human age identification via machine learning
| 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.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.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.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 |