Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition)

 

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dc.contributor.author Muller, Neil Leonard en_ZA
dc.date.accessioned 2015-10-28T05:29:55Z
dc.date.available 2015-10-28T05:29:55Z
dc.date.issued 1998 en_ZA
dc.identifier.citation Muller, N. 1998. Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition). University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/14381
dc.description Includes bibliographical references. en_ZA
dc.description.abstract In the first part of this dissertation, we present a detailed description of the eigenface technique first proposed by Sirovich and Kirby and subsequently developed by several groups, most notably the Media Lab at MIT. Other significant contributions have been made by Rockefeller University, whose ideas have culminated in a commercial system known as Faceit. For a different techniques (i.e. not eigenfaces) and a detailed comparison of some other techniques, the reader is referred to [5]. Although we followed ideas in the open literature (we believe there that there is a large body of advanced proprietary knowledge, which remains inaccessible), the implementation is our own. In addition, we believe that the method for updating the eigenfaces to deal with badly represented images presented in section 2. 7 is our own. The next stage in this section would be to develop an experimental system that can be extensively tested. At this point however, another, nonscientific difficulty arises, that of developing an adequately large data base. The basic problem is that one needs a training set representative of all faces to be encountered in future. Note that this does not mean that one can only deal with faces in the database, the whole idea is to be able to work with any facial image. However, a data base is only representative if it contains images similar to anything that can be encountered in future. For this reason a representative database may be very large and is not easy to build. In addition for testing purposes one needs multiple images of a large number of people, acquired over a period of time under different physical conditions representing the typical variations encountered in practice. Obviously this is a very slow process. Potentially the variation between the faces in the database can be large suggesting that the representation of all these different images in terms of eigenfaces may not be particularly efficient. One idea is to separate all the facial images into different, more or less homogeneous classes. Again this can only be done with access to a sufficiently large database, probably consisting of several thousand faces. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Applied Mathematics en_ZA
dc.title Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Mathematics and Applied Mathematics en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Muller, N. L. (1998). <i>Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition)</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics. Retrieved from http://hdl.handle.net/11427/14381 en_ZA
dc.identifier.chicagocitation Muller, Neil Leonard. <i>"Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition)."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 1998. http://hdl.handle.net/11427/14381 en_ZA
dc.identifier.vancouvercitation Muller NL. Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition). [Thesis]. University of Cape Town ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 1998 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/14381 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Muller, Neil Leonard AB - In the first part of this dissertation, we present a detailed description of the eigenface technique first proposed by Sirovich and Kirby and subsequently developed by several groups, most notably the Media Lab at MIT. Other significant contributions have been made by Rockefeller University, whose ideas have culminated in a commercial system known as Faceit. For a different techniques (i.e. not eigenfaces) and a detailed comparison of some other techniques, the reader is referred to [5]. Although we followed ideas in the open literature (we believe there that there is a large body of advanced proprietary knowledge, which remains inaccessible), the implementation is our own. In addition, we believe that the method for updating the eigenfaces to deal with badly represented images presented in section 2. 7 is our own. The next stage in this section would be to develop an experimental system that can be extensively tested. At this point however, another, nonscientific difficulty arises, that of developing an adequately large data base. The basic problem is that one needs a training set representative of all faces to be encountered in future. Note that this does not mean that one can only deal with faces in the database, the whole idea is to be able to work with any facial image. However, a data base is only representative if it contains images similar to anything that can be encountered in future. For this reason a representative database may be very large and is not easy to build. In addition for testing purposes one needs multiple images of a large number of people, acquired over a period of time under different physical conditions representing the typical variations encountered in practice. Obviously this is a very slow process. Potentially the variation between the faces in the database can be large suggesting that the representation of all these different images in terms of eigenfaces may not be particularly efficient. One idea is to separate all the facial images into different, more or less homogeneous classes. Again this can only be done with access to a sufficiently large database, probably consisting of several thousand faces. DA - 1998 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1998 T1 - Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) TI - Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) UR - http://hdl.handle.net/11427/14381 ER - en_ZA


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