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dc.contributor.authorCalvo Ortega, Guillermo
dc.contributor.authorBaruque, Bruno
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.date.accessioned2017-09-06T09:17:08Z
dc.date.available2017-09-06T09:17:08Z
dc.date.issued2013-09
dc.identifier.citationHybrid Artificial Intelligent Systems Lecture Notes in Computer Science. 8th International Conference, HAIS 2013, Salamanca, Spain, September 11-13, 2013. Proceedings. Lecture Notes in Computer Science. Volumen 8073, pp. 334-344.
dc.identifier.isbn978-3-642-40845-8 (Print) / 978-3-642-40846-5 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/135160
dc.description.abstractThe present work is a study of the influence of the preprocessing stage on the classification performance of a face recognition analysis. To carry out this task have made tests in a full FRS, evaluating each of its four stages and including several advanced alternatives in preprocessing, such as illumination normalization through the Discrete Cosine Transformation or alignment by Enhanced Correlation Coefficient, among others. The main goal of this work is determining how those different preprocessing alternatives interact with each other and in wich degree they affect the overall Facial Recognition Systems (FRS). The tests make a special emphasis in using images that could have been obtained from a real environment, rather than at a lab environment, with the difficulties that this brings for facial recognicion techniques.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleStudy of the Pre-processing Impact in a Facial Recognition System
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported