Title page for ETD etd-0114103-115012


Type of Document Dissertation
Author Scott, II, Willie L.
Author's Email Address wscott@lsu.edu
URN etd-0114103-115012
Title Block-Level Discrete Cosine Transform Coefficients for Autonomic Face Recognition
Degree Doctor of Philosophy (Ph.D.)
Department Engineering Science (Interdepartmental Program)
Advisory Committee
Advisor Name Title
Subhash Kak Committee Chair
Gerald Knapp Committee Co-Chair
Donald Kraft Committee Member
Jianhua Chen Committee Member
William Adkins Dean's Representative
Keywords
  • NoN model
  • face recognition
  • discrete cosine transform
  • DCT
  • autonomic
Date of Defense 2002-12-17
Availability unrestricted
Abstract
This dissertation presents a novel method of autonomic face recognition based on the recently proposed biologically plausible network of networks (NoN) model of information processing. The NoN model is based on locally parallel and globally coordinated transformations. In the NoN architecture, the neurons or computational units form distributed networks, which themselves link to form larger networks. In the general case, an n-level hierarchy of nested distributed networks is constructed. This models the structures in the cerebral cortex described by Mountcastle and the architecture based on that proposed for information processing by Sutton. In the implementation proposed in the dissertation, the image is processed by a nested family of locally operating networks along with a hierarchically superior network that classifies the information from each of the local networks. The implementation of this approach helps obtain sensitivity to the contrast sensitivity function (CSF) in the middle of the spectrum, as is true for the human vision system. The input images are divided into blocks to define the local regions of processing. The two-dimensional Discrete Cosine Transform (DCT), a spatial frequency transform, is used to transform the data into the frequency domain. Thereafter, statistical operators that calculate various functions of spatial frequency in the block are used to produce a block-level DCT coefficient. The image is now transformed into a variable length vector that is trained with respect to the data set. The classification was done by the use of a backpropagation neural network. The proposed method yields excellent results on a benchmark database. The results of the experiments yielded a maximum of 98.5% recognition accuracy and an average of 97.4% recognition accuracy. An advanced version of the method where the local processing is done on offset blocks has also been developed. This has validated the NoN approach and further research using local processing as well as more advanced global operators is likely to yield even better results.
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