Facial portraits matching by means of two-dimensional CCA and PLS

• Autorzy: Kukharev G. , Tujaka A. , Formaczyński P.
• Języki publikacji: EN

Abstrakty

EN

Paper presents implementation of the method of two-dimensional canonical correlation analysis and two-dimensional partial least squares applied to image matching. Both methods are based on representing the image as the sets of its rows and columns and implementation of CCA using these sets (hence we named the methods as CCArc and PLSrc). CCArc and PLSrc features simple implementation and lesser complexity than other known approaches. In applications to biometrics, CCArc and PLSrc are 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). The paper demonstrates high efficiency of CCArc and PLSrc for a number of computer experiments using benchmark image databases.

Słowa kluczowe

EN

two-dimensional canonical correlation analysis (CCArc); two-dimensional partial least squares; PLSrc; feature space dimensionality reduction; face matching

PL

dwuwymiarowa analiza korelacji kanonicznej; kwadrat cząstkowy dwuwymiarowy najmniejszy; PLSrc; redukcja wymiarowości cech przestrzeni; twarz pasująca

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Metody Informatyki Stosowanej
• Rocznik: 2010
• Tom: nr 1 (22)
• Strony: 69--86
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Kukharev G.

autor

Tujaka A.

autor

Formaczyński P.

• West Pomeranian University of Technology, Szczecin, Faculty of Computer Science and Information Technologies

Bibliografia

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