Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/490159
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dc.coverage.spatialIRIS Recognition
dc.date.accessioned2023-06-08T09:04:51Z-
dc.date.available2023-06-08T09:04:51Z-
dc.identifier.urihttp://hdl.handle.net/10603/490159-
dc.description.abstractBiometrics is trust-worthy and automatic recognition process which maps distinctive characteristics of an individual. Biometrics identifiers mainly use for realistic authentication and access control. Biometrics categorized in physiological and behavioral characteristics. In physiological characteristics which are related to body shapes. Examples of these characteristics are face, palm print, hand geometry, iris, fingerprint, DNA. Examples of behavioral characteristics are typing rhythm, gait and voice. Among these iris has described as ideal part of human body because iris is mainly protected internal organ of eye and it has random pattern of great complexity which has uniqueness. Iris pattern is always constant throughout life. Reliable personal identification using iris as a biometric is recently an active topic in the research area of pattern recognition. It is because of increase in the demand of enhanced security of human being in daily life. Automated iris recognition is yet another alternative for non-invasive verification and identification of people. Even though newlineDaugman [1-5] proved greatest accuracy using iris based human identification system, there is still problem with partial and important iris region selection for recognition of partially occluded iris images. Also there is need development of own iris image acquisition set up to acquire clear iris images to propose huge iris image database. Hence to overcome problem of partial and important iris region selection for recognition of partially occluded iris image due to lower eyelids, firstly we have implemented iris recognition algorithm using cumulative sums based change analysis by proposed partial iris from lower half part of the iris leaving 8 inner pixels from outer boundary of iris. Fuzzy hamming distance classifier is proposed as best classifier as compared to hamming distance classifier for the iris recognition using cumulative sums based change analysis. DCT based iris recognition algorithm have been implemented for the same proposed iris
dc.format.extent76p
dc.languageEnglish
dc.relation86b
dc.rightsuniversity
dc.titleIRIS Recognition for Personal Identification using Texture Based Analysis
dc.title.alternative
dc.creator.researcherKamble, Usha Ramdas
dc.subject.keywordEngineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Electrical and Electronic
dc.description.note
dc.contributor.guideWaghmare L. M.
dc.publisher.placeNanded
dc.publisher.universitySwami Ramanand Teerth Marathwada University
dc.publisher.institutionDepartment of Electronics and Telecommunication Engineering
dc.date.registered2008
dc.date.completed2021
dc.date.awarded2021
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Electronics and Telecommunication Engineering

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01_title.pdfAttached File66.29 kBAdobe PDFView/Open
02_certificate.pdf365.81 kBAdobe PDFView/Open
03_abstract.pdf60.37 kBAdobe PDFView/Open
04_decleration.pdf48.5 kBAdobe PDFView/Open
05_acknowledgement.pdf63.85 kBAdobe PDFView/Open
06_contents.pdf442.36 kBAdobe PDFView/Open
07_list of tables.pdf42.99 kBAdobe PDFView/Open
08_list of figures.pdf39.76 kBAdobe PDFView/Open
09_abbreviations.pdf277.56 kBAdobe PDFView/Open
10_chapter 1.pdf921.51 kBAdobe PDFView/Open
11_chapter 2.pdf1.21 MBAdobe PDFView/Open
12_chapter 3.pdf595.29 kBAdobe PDFView/Open
13_chapter 4.pdf933.35 kBAdobe PDFView/Open
14_chapter 5.pdf728.26 kBAdobe PDFView/Open
15_conclusions.pdf215.93 kBAdobe PDFView/Open
16_summary.pdf224.47 kBAdobe PDFView/Open
17_bibliography.pdf371.86 kBAdobe PDFView/Open
80_recommendation.pdf499 kBAdobe PDFView/Open


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