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A deep analysis on age estimation
dc.contributor.author | Huerta Casado, Iván |
dc.contributor.author | Fernandez Tena, Carles |
dc.contributor.author | Segura, Carlos |
dc.contributor.author | Hernando Pericás, Francisco Javier |
dc.contributor.author | Prati, Andrea |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2016-03-07T18:09:08Z |
dc.date.available | 2017-06-17T00:30:30Z |
dc.date.issued | 2015-12-15 |
dc.identifier.citation | Huerta, I., Fernandez, C., Segura, C., Hernando, J., Prati, A. A deep analysis on age estimation. "Pattern recognition letters", 15 Desembre 2015, vol. 68, núm. 2, p. 239-249. |
dc.identifier.issn | 0167-8655 |
dc.identifier.uri | http://hdl.handle.net/2117/83918 |
dc.description.abstract | The automatic estimation of age from face images is increasingly gaining attention, as it facilitates applications including advanced video surveillance, demographic statistics collection, customer profiling, or search optimization in large databases. Nevertheless, it becomes challenging to estimate age from uncontrollable environments, with insufficient and incomplete training data, dealing with strong person-specificity and high within-range variance. These difficulties have been recently addressed with complex and strongly hand-crafted descriptors, difficult to replicate and compare. This paper presents two novel approaches: first, a simple yet effective fusion of descriptors based on texture and local appearance; and second, a deep learning scheme for accurate age estimation. These methods have been evaluated under a diversity of settings, and the extensive experiments carried out on two large databases (MORPH and FRGC) demonstrate state-of-the-art results over previous work. |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::So, imatge i multimèdia |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
dc.subject.lcsh | Pattern recognition systems |
dc.subject.lcsh | Image processing -- Digital techniques |
dc.subject.other | Age estimation |
dc.subject.other | Deep learning |
dc.subject.other | DNN |
dc.subject.other | CCA |
dc.subject.other | HOG |
dc.subject.other | LBP |
dc.title | A deep analysis on age estimation |
dc.type | Article |
dc.subject.lemac | Reconeixement de formes (Informàtica) |
dc.subject.lemac | Imatges -- Processament -- Tècniques digitals |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.identifier.doi | 10.1016/j.patrec.2015.06.006 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0167865515001683 |
dc.rights.access | Open Access |
local.identifier.drac | 17527001 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Huerta, I.; Fernandez, C.; Segura, C.; Hernando, J.; Prati, A. |
local.citation.publicationName | Pattern recognition letters |
local.citation.volume | 68 |
local.citation.number | 2 |
local.citation.startingPage | 239 |
local.citation.endingPage | 249 |
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