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dc.contributor.advisorFargues, Monique P.
dc.contributor.advisorCristi, Roberto
dc.contributor.advisorKarunasiri, Gamani
dc.contributor.authorDomboulas, Dimitrios I.
dc.date.accessioned2012-03-14T17:31:15Z
dc.date.available2012-03-14T17:31:15Z
dc.date.issued2004-12
dc.identifier.urihttp://hdl.handle.net/10945/1315
dc.descriptionApproved for public release; distribution in unlimited.en_US
dc.description.abstractIn recent years there has been an increased interest in effective individual control and enhanced security measures, and face recognition schemes play an important role in this increasing market. In the past, most face recognition research studies have been conducted with visible imaging data. Only recently have IR imaging face recognition studies been reported for wide use applications, as uncooled IR imaging technology has improved to the point where the resolution of these much cheaper cameras closely approaches that of cooled counterparts. This study is part of an on-going research conducted at the Naval Postgraduate School which investigates the feasibility of applying a low cost uncooled IR camera for face recognition applications. This specific study investigates whether nonlinear kernel-based classifiers may improve overall classification rates over those obtained with linear classification schemes. The study is applied to a 50 subject IR database developed in house with a low resolution uncooled IR camera. Results show best overall mean classification performances around 98.55% which represents a 5% performance improvement over the best linear classifier results obtained previously on the same database. This study also considers several metrics to evaluate the impacts variations in various user-specified parameters have on the resulting classification performances. These results show that a low-cost, low-resolution IR camera combined with an efficient classifier can play an effective role in security related applications.en_US
dc.description.urihttp://archive.org/details/infraredimagingf109451315
dc.format.extentxvi, 111 p. : ill. (some col.) ;en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsCopyright is reserved by the copyright owner.en_US
dc.subject.lcshHuman face recognition (Computer science)en_US
dc.subject.lcshPattern recognition systemsen_US
dc.subject.lcshInfrared imagingen_US
dc.subject.lcshEigenvectorsen_US
dc.titleInfrared imaging face recognition using nonlinear kernel-based classifiersen_US
dc.typeThesisen_US
dc.contributor.corporateNaval Postgraduate School (U.S.).
dc.contributor.departmentElectrical and Computer Engineering
dc.subject.authorFace Recognitionen_US
dc.subject.authorPattern Classificationen_US
dc.subject.authorInfrareden_US
dc.subject.authorGDAen_US
dc.subject.authorDistancesen_US
dc.subject.authorEigenvectors.en_US
dc.description.serviceCaptain, Hellenic Air Forceen_US
etd.thesisdegree.nameM.S. in Electrical Engineeringen_US


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