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Title: Computer recognition of human face image using nonlinear dynamical system and wavelet based artificial light receptor models
Researcher: Kabeer, V
Guide(s): Narayanan, N K
Keywords: Wavelet based Artificial Light Receptor Model
ALR feature vector
Information technology
Computer Science
Upload Date: 1-Sep-2011
University: Kannur University
Completed Date: 2010
Abstract: The problem of automatic holistic analysis, understanding and recognition of face images are addressed in this thesis. Face recognition lies at the core of the discipline of pattern recognition and face recognition system is expected to identify faces present in images and videos automatically. Two novel and new methods for representation of face images are proposed in this thesis. Two new features based on the new representations are also introduced by this work. The proposed representations are proved to be effective in capturing intraclass and interclass variations and hence are useful for developing face recognition systems. Based on these new representations, novel methods of face recognition are investigated. A comparatively large face image database consisting of 6600 face image samples of 110 individuals using mobile camera is created for recognition study. In addition to this database standard AT&T face database is also used for comparative study. A novel, robust and new paradigm for feature extraction based on nonlinear dynamical system model for face recognition has been investigated in this thesis. To this end Reconstructed State Spaces for face images are generated using different space variations. The method of extracting State Space Point Distribution (SSPD)features from the reconstructed state space of face samples are also designed. Another novel method of the representation for face images using a new biologically inspired model (Wavelet based Artificial Light Receptor Model ) is investigated and presented in this thesis. The knowledge about the distribution of light receptors, cones and rods, over the surface of the retina, and the way how they are associated with the nerve ends for pattern vision forms the basis i for the design of this model.
Pagination: iii, 226p.
Appears in Departments:Department of Information Technology

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01_title.pdfAttached File118.49 kBAdobe PDFView/Open
02_certificate.pdf40.8 kBAdobe PDFView/Open
03_declaration.pdf40.58 kBAdobe PDFView/Open
04_dedication.pdf20.54 kBAdobe PDFView/Open
05_table of contents.pdf28.7 kBAdobe PDFView/Open
06_abstract.pdf35.94 kBAdobe PDFView/Open
07_acknowledgement.pdf34.73 kBAdobe PDFView/Open
08_chapter 1.pdf50.63 kBAdobe PDFView/Open
09_chapter 2.pdf104.95 kBAdobe PDFView/Open
10_chapter 3.pdf2.61 MBAdobe PDFView/Open
11_chapter 4.pdf4.9 MBAdobe PDFView/Open
12_chapter 5.pdf39.44 MBAdobe PDFView/Open
13_chapter 6.pdf684.01 kBAdobe PDFView/Open
14_chapter 7.pdf243.47 kBAdobe PDFView/Open
15_chapter 8.pdf334.36 kBAdobe PDFView/Open
16_chapter 9.pdf87.35 kBAdobe PDFView/Open
17_chapter 10.pdf109.17 kBAdobe PDFView/Open
18_list of publications.pdf37.66 kBAdobe PDFView/Open
19_bibliography.pdf86.18 kBAdobe PDFView/Open

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