Title page for etd-0703106-165315


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URN etd-0703106-165315
Author Wei-li Chou
Author's Email Address No Public.
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Department Applied Mathematics
Year 2005
Semester 2
Degree Master
Type of Document
Language English
Title Geometric Transformation and Illumination Invariant for Facial Recognition
Date of Defense 2006-05-18
Page Count 29
Keyword
  • illumination effect
  • least squares method
  • perspective transformation
  • face recognition
  • geometric transformation
  • Abstract There exist many methods for facial recognition, such as
    eigenface, templates, artificial neural networks, etc., based on the given facial sample data (patterns). When an input facial image (target) involve simple geometrical transformations and illumination, the performance of these methods are not very satisfactory. In this thesis, following Li et al., we propose a new face recognition system, which can eliminate translation, rotation, scaling, and prospective transformations of facial images automatically, and can also eliminate illumination. According to facial features, we use this method to find the best transformation and the closet illumination, and then to eliminate them for identification by the best matching between a target and the patterns. Finally, we
    use the least squares method to recognize the target. This method is validated by numerical examples.
    Advisory Committee
  • Yung-Nien Sun - chair
  • Chung-nan Lee - co-chair
  • Song Wang - co-chair
  • Y. W. Chiang - co-chair
  • Zi-Cai Li - advisor
  • Files
  • etd-0703106-165315.pdf
  • indicate access worldwide
    Date of Submission 2006-07-03

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