Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/9103
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dc.coverage.spatialElectronicsen_US
dc.date.accessioned2013-05-23T09:23:53Z-
dc.date.available2013-05-23T09:23:53Z-
dc.date.issued2013-05-23-
dc.identifier.urihttp://hdl.handle.net/10603/9103-
dc.description.abstractThis thesis is an attempt to unravel the problem of human face recognition. Face recognition is a biometric authentication method that has become more and more relevant in the recent years. Face recognition is a popular research area where there are different approaches studied in the literature.In this thesis face recognition problem is handled by applying Principal Component Analysis (PCA), Various Orthogonal transforms and different Vector Quantization (VQ) codebook generation techniques. The eigenvectors are the principal component of the set of images. Each face can be closely represented by a linear combination of the Eigenfaces. The Eigen face method tries to find a lower dimensional space for the representation of the face images. Here to reduce the dimensionality instead of utilizing all the Eigenfaces for recognition purpose only small number of Eigenfaces is selected according to the energy contain which is close to total energy of original face. This particular method helps to reduce the dimensionality efficiently. The main drawback of PCA is scalability. As dataset changes the whole eigenspace distribution also changes. To avoid this difficulty various orthogonal transforms like Discrete Cosine Transform, Discrete Sine Transform, Walsh Hadamard Transform, Slant Transform, Wavelet Transform and newly proposed Kekre s Transform are applied on controlled standard ORL database and unconstrained local database prepared by using Indian origin faces of 50 male and 50 female. The concept of image energy compaction in low frequency coefficients is explored here. Instead of using total image energy and all the transformed coefficients for comparing a test image with the stored database only selected number of coefficients according to desired energy of image is utilized for recognition purpose. To reduce computational time and complexity the separablity property of transforms is utilized.en_US
dc.format.extent193p.en_US
dc.languageEnglishen_US
dc.relationNo. of references 118en_US
dc.rightsuniversityen_US
dc.titleFace recognition using Orthogonal Transforms and Vector Quantization Techniquesen_US
dc.creator.researcherShah, Kamalen_US
dc.subject.keywordFace Recognitionen_US
dc.subject.keywordOrthogonal Transformsen_US
dc.subject.keywordVector Quantizationen_US
dc.description.noteReferences p. 181-193, Appendix includeden_US
dc.contributor.guideKekre, H Ben_US
dc.publisher.placeMumbaien_US
dc.publisher.universityNarsee Monjee Institute of Management Studiesen_US
dc.publisher.institutionDepartment of Electronic Engineeringen_US
dc.date.registered28/08/2006en_US
dc.date.completed31/07/2010en_US
dc.date.awarded31/07/2010en_US
dc.format.dimensions--en_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Department of Electronic Engineering

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01_title.pdfAttached File113.42 kBAdobe PDFView/Open
02_ table of contents.pdf172.63 kBAdobe PDFView/Open
03_abstract.pdf90.17 kBAdobe PDFView/Open
04_chapter 1.pdf495.67 kBAdobe PDFView/Open
05_chapter 2.pdf258.98 kBAdobe PDFView/Open
06_chapter 3.pdf927.97 kBAdobe PDFView/Open
07_chapter 4-a.pdf373.95 kBAdobe PDFView/Open
08_chapter 4-b.pdf532.79 kBAdobe PDFView/Open
09_chapter 5.pdf571.58 kBAdobe PDFView/Open
10_chapter 6.pdf130.75 kBAdobe PDFView/Open
11_references.pdf138.37 kBAdobe PDFView/Open
12_appendix.pdf8.27 MBAdobe PDFView/Open


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