Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/256805
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dc.coverage.spatialElectronics and Telecommunication
dc.date.accessioned2019-09-09T08:41:54Z-
dc.date.available2019-09-09T08:41:54Z-
dc.identifier.urihttp://hdl.handle.net/10603/256805-
dc.description.abstractFace recognition system is a kind of biometric system which may or may not require the co-operation of the participant. Whereas the other biometric systems such as fingerprint recognition, retina or iris based recognition need the active co-operation of the participant. Being an example of pattern recognition system, all the steps involved in pattern recognition system are equally applicable in face recognition system. Normally all the face analysis methods are classified into two types- face verification and face recognition. In the verification mode the claimed identity of a person is verified. Whereas in a recognition mode, the person s identity is matched with the one in the database. newlineOver the last three decades number of algorithms are suggested by various researchers for face recognition. The researchers from the diverse fields like computer science, neuroscience and psychology have a huge interest in it. Active research is going on in face recognition and a notable progress is achieved in various phases of face recognition like segmentation, feature extraction and classification. But the robust face recognition is still to be achieved. The multiple evaluation tests and grand challenges like FERET, FVRT and FRGC pointed out that the illumination and the pose variation are the main challenges in the successful implementation of a face recognition system. newlineWe propose a face recognition system based on Gabor wavelet, as it proved the best in the feature extraction. Gabor wavelet bank contains a set of narrowband filters with different bandwidth. With the help of these filters extract the features present in the image and then use these features for further processing. The Gabor features extracted have high dimensionality. For the dimensionality reduction subspace analysis techniques using kernel linear discriminant analysis is used. The use of the Gaussian kernel for projecting the features into a high valued features to make them easy to discriminate with linear discriminant analysis. To reduce the dimensionalit
dc.format.extent92p
dc.language-1
dc.relation97b
dc.rightsuniversity
dc.titleIllumination and pose Invariant Face Recognition
dc.title.alternativen.a.
dc.creator.researcherSharma Kailash Shriram
dc.subject.keywordEngineering and Technology,Engineering,Engineering Electrical and Electronic
dc.description.noteBibliography
dc.contributor.guideManthalkar R. R.
dc.publisher.placeNanded
dc.publisher.universitySwami Ramanand Teerth Marathwada University
dc.publisher.institutionFaculty of Engineering
dc.date.registered07/09/2010
dc.date.completed2018
dc.date.awarded04/09/2019
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Engineering

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01_title.pdfAttached File187.07 kBAdobe PDFView/Open
02_certificate.pdf175.61 kBAdobe PDFView/Open
03_abstract.pdf182.3 kBAdobe PDFView/Open
04_declaration.pdf180.92 kBAdobe PDFView/Open
05_acknowledgement.pdf84.24 kBAdobe PDFView/Open
06_contents.pdf26.84 kBAdobe PDFView/Open
07_list_of_tables.pdf10.79 kBAdobe PDFView/Open
08_list_of_figures.pdf116.39 kBAdobe PDFView/Open
09_abbreviations.pdf5.17 kBAdobe PDFView/Open
10_chapter 1.pdf192.73 kBAdobe PDFView/Open
11_chapter 2.pdf573.36 kBAdobe PDFView/Open
12_chapter 3.pdf792.54 kBAdobe PDFView/Open
13_chapter 4.pdf675.09 kBAdobe PDFView/Open
14_chapter 5.pdf565.31 kBAdobe PDFView/Open
15_chapter 6.pdf459.25 kBAdobe PDFView/Open
16_conclusion.pdf198.95 kBAdobe PDFView/Open
17_bibliography.pdf295.16 kBAdobe PDFView/Open


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