Please use this identifier to cite or link to this item: https://dspace.univ-ouargla.dz/jspui/handle/123456789/19053
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAmina, Mokadem-
dc.contributor.authorD, SAMAI-
dc.contributor.authorK, BEN SID-
dc.date.accessioned2018-09-24T11:28:39Z-
dc.date.available2018-09-24T11:28:39Z-
dc.date.issued2018-09-24-
dc.identifier.urihttp://dspace.univ-ouargla.dz/jspui/handle/123456789/19053-
dc.descriptionPeople's Democratic Republic of Algeria University Kasdi Merbah-Ouargla Faculty of New Technologies of Information and Communication Department of Electronic and Telecommunicationen_US
dc.description.abstractOne of the current trends in human identification is the development of new emerging methods. Due to increased security concerns and the development of counterfeiting techniques. This development depends on the unique parts of the human body that can be identified and used as a means of identifying a person. Including fingerprints, iris, lips, etc. Most of the systems and methods are slow or require expensive technical equipment, for this, we suggest a new approach for personal authentication using Finger-Knuckle Print through with a novel texture descriptor, Discrete Cosine Transform Network (DCTNet) and support vector machine (SVM) classifier. Fingerknuckle- print is one of the emerging biometric traits.Recently it has been found FKP is highly rich in textures and can be used to uniquely identify a person. The study also takes the unimodal and multi-modal biometric systems results along with their methods of information fusion in score level, which does not require special equipment and can be used in systems where fast detection is needed. Our methods significantly out performs stateof the art methods.en_US
dc.language.isoenen_US
dc.subjectBiometricen_US
dc.subjectFKPen_US
dc.subjectDCTNeten_US
dc.subjectSVMen_US
dc.subjectunimodalen_US
dc.subjectmultimodalen_US
dc.titleEfficient person identification by Finger-Knuckle-Print based on Discrete Cosine Transform Networken_US
dc.typeOtheren_US
Appears in Collections:Département d'Electronique et des Télécommunications - Master

Files in This Item:
File Description SizeFormat 
Mokadem - Amina.pdf1,93 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.