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dc.contributor.authorGaldámez, Pedro L.
dc.contributor.authorGonzález Arrieta, María Angélica 
dc.date.accessioned2017-09-06T09:13:51Z
dc.date.available2017-09-06T09:13:51Z
dc.date.issued2013/05
dc.identifier.citationDistributed Computing and Artificial Intelligence. 10th International Conference. Advances in Intelligent Systems and Computing. Volumen 217, pp. 393-400.
dc.identifier.isbn978-3-319-00550-8(Print) / 978-3-319-00551-5(Online)
dc.identifier.issn2194-5357(Print) / 2194-5365(Online)
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-00551-5_48
dc.identifier.urihttp://hdl.handle.net/10366/134816
dc.description.abstractThis document provides an approach to biometrics analysis which consists in the location and identification of faces in real time, making the concept a safe alternative to Web sites based on the paradigm of user and password. Numerous techniques are available to implement face recognition including the principal component analysis (PCA), neural networks, and geometric approach to the problem considering the shapes of the face representing a collection of values. The study and application of these processes originated the development of a security architecture supported by the comparison of images captured from a webcam using methodology of PCA, and the Hausdorff algorithm of distance as similarity measures between a general model of the registered user and the objects (faces) stored in the database, the result is a web authentication system with main emphasis on efficiency and application of neural networks.
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.titleFace Identification by Real-Time Connectionist System
dc.typeinfo:eu-repo/semantics/article
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