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2009 | nr 3 (20) | 103-112
Tytuł artykułu

Two-dimensional canonical correlation analysis for face image processing and recognition

Warianty tytułu
Języki publikacji
Paper presents the method of two-dimensional canonical correlation analysis (2DCCA) as applied to image processing and biometrics. Method is based on representing the image as the sets of its rows (r) and columns (c) and implementation of CCA using these sets (for these reason we named the method as CCArc). CCArc features simple implementation and lesser complexity than other known approaches. In applications to biometrics CCArc is suitable to solving the problems when dimension of images (dimension of feature space) is greater than number of images, i.e. Small Sample Size problem (SSS). Demonstrated high efficiency of CCArc method for a number of computer experiments. Experiments itself are described with compact notation allowing to use its results in the framework of meta-analysis.

Opis fizyczny
Bibliogr. 13 poz., rys.
  • West Pomeranian University of Technology, Szczecin, Poland, Faculty of Computer Science and Information Technology
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