Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1775
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bolis, Dimitris | - |
dc.contributor.author | Tefas, Anastasios | - |
dc.contributor.author | Pitas, Ioannis K. | - |
dc.contributor.author | Maronidis, Anastasios | - |
dc.date.accessioned | 2013-02-18T13:46:33Z | en |
dc.date.accessioned | 2013-05-16T13:11:27Z | - |
dc.date.accessioned | 2015-12-02T09:45:14Z | - |
dc.date.available | 2013-02-18T13:46:33Z | en |
dc.date.available | 2013-05-16T13:11:27Z | - |
dc.date.available | 2015-12-02T09:45:14Z | - |
dc.date.issued | 2011-10 | - |
dc.identifier.citation | Neural Networks, 2011, vol. 24, no. 8, pp. 814–823 | en_US |
dc.identifier.issn | 18792782 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/1775 | - |
dc.description.abstract | In this paper, the robustness of appearance-based subspace learning techniques in geometrical transformations of the images is explored. A number of such techniques are presented and tested using four facial expression databases. A strong correlation between the recognition accuracy and the image registration error has been observed. Although it is common-knowledge that appearance-based methods are sensitive to image registration errors, there is no systematic experiment reported in the literature. As a result of these experiments, the training set enrichment with translated, scaled and rotated images is proposed for confronting the low robustness of these techniques in facial expression recognition. Moreover, person dependent training is proven to be much more accurate for facial expression recognition than generic learning. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Neural Networks | en_US |
dc.rights | © Elsevier | en_US |
dc.subject | Facial expression recognition | en_US |
dc.subject | Appearance based techniques | en_US |
dc.subject | Subspace learning methods | en_US |
dc.title | Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets | en_US |
dc.type | Article | en_US |
dc.affiliation | Aristotle University of Thessaloniki | en |
dc.collaboration | Aristotle University of Thessaloniki | en_US |
dc.subject.category | Arts | en_US |
dc.subject.category | Other Humanities | en_US |
dc.journals | Subscription | en_US |
dc.country | Greece | en_US |
dc.subject.field | Humanities | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1016/j.neunet.2011.05.015 | en_US |
dc.dept.handle | 123456789/54 | en |
dc.relation.issue | 8 | en_US |
dc.relation.volume | 24 | en_US |
cut.common.academicyear | 2011-2012 | en_US |
dc.identifier.spage | 814 | en_US |
dc.identifier.epage | 823 | en_US |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 0893-6080 | - |
crisitem.journal.publisher | Elsevier | - |
crisitem.author.dept | Department of Multimedia and Graphic Arts | - |
crisitem.author.faculty | Faculty of Fine and Applied Arts | - |
crisitem.author.orcid | 0000-0001-9656-8685 | - |
crisitem.author.parentorg | Faculty of Fine and Applied Arts | - |
Appears in Collections: | Άρθρα/Articles |
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