| dc.contributor.author | Ramalingam, Soodamani | |
| dc.contributor.author | Garcia, Fabio | |
| dc.date.accessioned | 2021-07-21T09:39:26Z | |
| dc.date.available | 2021-07-21T09:39:26Z | |
| dc.date.issued | 2018-12-24 | |
| dc.identifier.citation | Ramalingam , S & Garcia , F 2018 , Facial Expression Recognition using Transfer Learning . in 2018 International Carnahan Conference on Security Technology (ICCST) Proceedings . Institute of Electrical and Electronics Engineers (IEEE) , 52nd Annual ICCST - 2018 International Carnahan Conference on Security Technology (ICCST) , Montreal , Quebec , Canada , 22/10/18 . https://doi.org/10.1109/CCST.2018.8585504 | |
| dc.identifier.citation | conference | |
| dc.identifier.isbn | 9781538679326 | |
| dc.identifier.isbn | 9781538679319 | |
| dc.identifier.uri | http://hdl.handle.net/2299/24860 | |
| dc.description | © 2021 IEEE - All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/CCST.2018.8585504 | |
| dc.description.abstract | In this paper, we investigate Deep Learning architectures for the recognition of facial expressions. In particular, we consider the concept of Transfer Learning whereby features learnt from generic images of large scale datasets can be used to train models of smaller databases without losing the generalization ability. | en |
| dc.format.extent | 5 | |
| dc.format.extent | 300943 | |
| dc.language.iso | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
| dc.relation.ispartof | 2018 International Carnahan Conference on Security Technology (ICCST) Proceedings | |
| dc.subject | Facial Expression Recognition | |
| dc.subject | Deep Learning | |
| dc.subject | General Computer Science | |
| dc.title | Facial Expression Recognition using Transfer Learning | en |
| dc.contributor.institution | Smart Mobility Unit | |
| dc.contributor.institution | Communications and Intelligent Systems | |
| dc.contributor.institution | Centre for Engineering Research | |
| dc.contributor.institution | School of Physics, Engineering & Computer Science | |
| dc.contributor.institution | Department of Engineering and Technology | |
| rioxxterms.versionofrecord | 10.1109/CCST.2018.8585504 | |
| rioxxterms.type | Other | |
| herts.preservation.rarelyaccessed | true | |
