Rule based segmentation and subject identification using fiducial features and subspace projection methods
dc.citation.epage | 75 | en_US |
dc.citation.issueNumber | 4 | en_US |
dc.citation.spage | 68 | en_US |
dc.citation.volumeNumber | 2 | en_US |
dc.contributor.author | Ince, E. A. | en_US |
dc.contributor.author | Ali, S. A. | en_US |
dc.date.accessioned | 2016-02-08T10:14:03Z | |
dc.date.available | 2016-02-08T10:14:03Z | |
dc.date.issued | 2007 | en_US |
dc.department | Computer Technology and Information Systems | en_US |
dc.description.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. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T10:14:03Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2007 | en |
dc.identifier.issn | 1796-203X | |
dc.identifier.uri | http://hdl.handle.net/11693/23446 | |
dc.language.iso | English | en_US |
dc.publisher | Academy Publisher | en_US |
dc.source.title | Journal of Computers | en_US |
dc.subject | Color FERET database | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Geometric normalization | en_US |
dc.subject | Skin color segmentation | en_US |
dc.subject | Subspace analysis methods | en_US |
dc.subject | Distance metrics | en_US |
dc.subject | Euclidean | en_US |
dc.subject | Eye localization | en_US |
dc.subject | FERET database | en_US |
dc.subject | Fisher-faces | en_US |
dc.subject | Geometric normalization | en_US |
dc.subject | Multiple components | en_US |
dc.subject | Nearest-neighbour classifier | en_US |
dc.subject | Number of shots | en_US |
dc.subject | Performance analysis | en_US |
dc.subject | Region segmentation | en_US |
dc.subject | Rule based | en_US |
dc.subject | Scale and rotation | en_US |
dc.subject | Skin-color segmentation | en_US |
dc.subject | Small samples | en_US |
dc.subject | Subject identification | en_US |
dc.subject | Subspace analysis | en_US |
dc.subject | Subspace projection methods | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Face recognition | en_US |
dc.title | Rule based segmentation and subject identification using fiducial features and subspace projection methods | en_US |
dc.type | Article | en_US |
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