Kernel Based Subspace Methods : Infrared vs Visible Face Recognition

• Autorzy: Shahbe M. D. , Hati S.
• Języki publikacji: EN

Abstrakty

EN

This paper investigates the use of kernel theory in two well-known, linear-based subspace representations: Principle Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (FLD). The kernel-based method provides subspaces of high-dimensional feature spaces induced by some nonlinear mappings. The focus of this work is to evaluate the performances of Kernel Principle Component Analysis (KPCA) and Kernel Fisher's Linear Discriminant Analysis (KFLD) for infrared (IR) and visible face recognition. The performance of the kernel-based subspace methods is compared with that of the conventional linear algorithms: PCA and FLD. The main contribution of this paper is the evaluation of the sensitivities of both IR and visible face images to illumination conditions, facial expressions and facial occlusions caused by eyeglasses using the kernel-based subspace methods.

Słowa kluczowe

EN

face recognition; principle component analysis; linear discriminant analysis; kernel methods

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2009
• Tom: Vol. 18, No. 1
• Strony: 47--66
• Opis fizyczny: Bibliogr. 23 poz., il., wykr.

Twórcy

autor

Shahbe M. D.

autor

Hati S.

• Faculty of Information Technology, Multimedia University, Malaysia

Bibliografia

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