Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/354098
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dc.date.accessioned2022-01-04T04:59:04Z-
dc.date.available2022-01-04T04:59:04Z-
dc.identifier.urihttp://hdl.handle.net/10603/354098-
dc.description.abstractABSTRACT newline newlineThe main objective of the project is to develop a biometric verification system using ear and fingerprint to identify a genuine user .In this contest I proposed two novel methods to improve accuracy. newlineIn initial method First, a pre-processing phase by image enhancement and thinning method, all the images have the samesize. Then, a feature extraction technique includes Minutiae and Singular point technique for fingerprint images. Ear features are extracted by using Speed Up Robust Features (SURF) and Binary Robust Invariant Scalable Key points (BRISK) techniques are used to determine the ear and fingerprint features. Fusion at the feature level is carried out through concatenation for features. At last, matching is carried out by registration and similarity score process, then by using the threshold values, the user is identified as genuine or an imposter. newlineIn second method, there are three important levels involved in the biometric detection which includes Preprocessing, Feature extraction and Segmentation. The novel technique begins with the preprocessing phase including the median filter which gives a helping hand to the job of cropping the image for selecting the position. The preprocessed images are extracted with the assistance of the feature extraction phase in which the shapes and texture features of the face and ear images are efficiently extracted. The consistent images are extracted with the aid of the LGXP approach. Thus, in the feature extraction phase, various features like the shape, texture and ear as well as finger print images are extracted. Later, the extracted features are integrated. The integrated features, in turn, are properly classified by the firefly algorithm in accordance with the distance. newlineThe experimental results showed that the proposed multi-modal biometric methods achieved 95.66% and 96.28% accuracies. The novel techniques are effectively performed in the MATLABplatform. newline
dc.format.extentxvi 115
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleMultimodal Biometric Cryptosystem for Human Authentication using Fingerprint and Ear
dc.title.alternative
dc.creator.researcherChanukya Padira SVVN
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideThivakaran TK
dc.publisher.placeChennai
dc.publisher.universityMeenakshi Academy of Higher Education and Research
dc.publisher.institutionDepartment of Engineering
dc.date.registered2015
dc.date.completed2019
dc.date.awarded2020
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Engineering

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01_title.pdfAttached File267.85 kBAdobe PDFView/Open
02_certificate.pdf37.44 kBAdobe PDFView/Open
03_declaration.pdf52.81 kBAdobe PDFView/Open
04_chapter 1.pdf1.7 MBAdobe PDFView/Open
05_chapter 2.pdf680.85 kBAdobe PDFView/Open
06_chapter 3.pdf3.4 MBAdobe PDFView/Open
07_chapter 4.pdf1.23 MBAdobe PDFView/Open
08_chapter 5.pdf598.13 kBAdobe PDFView/Open
09_chapter 6.pdf433.11 kBAdobe PDFView/Open
10_bibiliograpy.pdf558.75 kBAdobe PDFView/Open
11_annexure.pdf2.09 MBAdobe PDFView/Open
12_content.pdf271.47 kBAdobe PDFView/Open
13_list of tables and figures.pdf186.88 kBAdobe PDFView/Open
80_recommendation.pdf623.69 kBAdobe PDFView/Open


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