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Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition)
Includes bibliographical references.
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
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Department of Mathematics and Applied Mathematics
2015
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| _version_ | 1867611289300238336 |
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| access_status_str | Open Access |
| author | Muller, Neil Leonard |
| author_browse | Muller, Neil Leonard |
| author_facet | Muller, Neil Leonard |
| author_sort | Muller, Neil Leonard |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/14381 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Mathematics and Applied Mathematics |
| publisherStr | Department of Mathematics and Applied Mathematics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/14381 Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) Muller, Neil Leonard Applied Mathematics Includes bibliographical references. 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. 2015-10-28T05:29:55Z 2015-10-28T05:29:55Z 1998 Master Thesis Masters MSc http://hdl.handle.net/11427/14381 eng application/pdf Department of Mathematics and Applied Mathematics Faculty of Science University of Cape Town |
| spellingShingle | Applied Mathematics Muller, Neil Leonard Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) |
| thesis_degree_str | Master's |
| title | Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) |
| title_full | Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) |
| title_fullStr | Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) |
| title_full_unstemmed | Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) |
| title_short | Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) |
| title_sort | image recognition using the eigenpicture technique with specific applications in face recognition and optical character recognition |
| topic | Applied Mathematics |
| url | http://hdl.handle.net/11427/14381 |
| work_keys_str_mv | AT mullerneilleonard imagerecognitionusingtheeigenpicturetechniquewithspecificapplicationsinfacerecognitionandopticalcharacterrecognition |