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      Rule based segmentation and subject identification using fiducial features and subspace projection methods

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      Author(s)
      Ince, E. A.
      Ali, S. A.
      Date
      2007
      Source Title
      Journal of Computers
      Print ISSN
      1796-203X
      Publisher
      Academy Publisher
      Volume
      2
      Issue
      4
      Pages
      68 - 75
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      This paper describes a framework for carrying out face recognition on a subset of standard color FERET database using two different subspace projection methods, namely PCA and Fisherfaces. At first, a rule based skin region segmentation algorithm is discussed and then details about eye localization and geometric normalization are given. The work achieves scale and rotation invariance by fixing the inter ocular distance to a selected value and by setting the direction of the eye-to-eye axis. Furthermore, the work also tries to avoid the small sample space (S3) problem by increasing the number of shots per subject through the use of one duplicate set per subject. Finally, performance analysis for the normalized global faces, the individual extracted features and for a multiple component combination are provided using a nearest neighbour classifier with Euclidean and/or Cosine distance metrics.
      Keywords
      Color FERET database
      Feature extraction
      Geometric normalization
      Skin color segmentation
      Subspace analysis methods
      Distance metrics
      Euclidean
      Eye localization
      FERET database
      Fisher-faces
      Geometric normalization
      Multiple components
      Nearest-neighbour classifier
      Number of shots
      Performance analysis
      Region segmentation
      Rule based
      Scale and rotation
      Skin-color segmentation
      Small samples
      Subject identification
      Subspace analysis
      Subspace projection methods
      Feature extraction
      Face recognition
      Permalink
      http://hdl.handle.net/11693/23446
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      • Computer Technology and Information Systems 63
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