Performance Analysis and Recognition of Iris Patterns for Human Authentication Using a Modified Approach

dc.contributor.guideDr.A.Muruganen_US
dc.coverage.spatialPerformance Analysis and Recognition of Iris Patternsen_US
dc.creator.researcherG.Savithrien_US
dc.date.accessioned2014-03-18T04:55:53Z
dc.date.available2014-03-18T04:55:53Z
dc.date.awarded28/01/2014en_US
dc.date.completed29/08/2013en_US
dc.date.issued2014-03-18
dc.date.registered24/02/2007en_US
dc.description.abstractWith the increasing demand for profound security in our daily lives, reliable newlinepersonal identification through biometrics is currently an active topic in the literature of newlinepattern recognition. Iris recognition is one of important biometric recognition approach in a newlinehuman identification that is becoming very active topic in research and practical newlineapplication. Once the image of the iris has been captured using a standard camera, the newlineauthentication process, involving the comparison of current subject s iris with the stored newlineversion, is one of the most accurate with very low false acceptance and rejection rates. newlineThe main aim of the thesis is to study about iris recognition system which includes iris newlinelocalization and normalization by using rubber sheet model, feature extraction using Gabor newlineWavelet as well as template matching by Hamming distance. Compression technique is used newlineto compress the eye image and this compressed eye is used for the localization of the inner newlineand outer boundaries of the iris region. We investigated that the effect of compression on iris newlinerecognition system accurately identifies individual s using different distance measures. newlineIn this thesis work, we proved that it is possible to improve the reliability of the newlinesystem by choosing a portion of the iris instead of whole extension of the iris. Initially, newlineportion of the iris pattern is extracted using Gabor Wavelet(GW) and later, different newlinetechniques such as Histogram of Oriented Gradient (HOG) and Local Binary Pattern newline(LBP) are used for feature extraction to identify a person in successful manner with low newlinefalse acceptance rate and with low false rejection rate. newlineFinally, the iris features extracted using GW, HOG and LBP are taken as input and newlinefed to Back Propagation Neural Network for classification. We implemented prominent newlineiris recognition algorithm in MATLAB. newlineThe system is to be composed of a number of sub-systems which correspond to each stage of iris recognition.en_US
dc.description.notereference p. 110 - 124en_US
dc.format.accompanyingmaterialCDen_US
dc.format.dimensionsA4en_US
dc.format.extent124en_US
dc.identifier.urihttp://hdl.handle.net/10603/17476
dc.languageEnglishen_US
dc.publisher.institutionDepartment of Computer Scienceen_US
dc.publisher.placeKodaikanalen_US
dc.publisher.universityMother Teresa Womens Universityen_US
dc.relation225en_US
dc.rightsuniversityen_US
dc.source.universityUniversityen_US
dc.subject.keywordcomputer, Performance Analysis, Iris, Humanen_US
dc.titlePerformance Analysis and Recognition of Iris Patterns for Human Authentication Using a Modified Approachen_US
dc.title.alternativeNilen_US
dc.type.degreePh.D.en_US

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