Gabor-Kernel fisher analysis for face recognition

Authors:
Baochang Zhang
Affiliations:
Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China
Venue:
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Year:
2004

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Abstract

Kernel based methods have been of wide concern in the field of machine learning. This paper proposes a novel Gabor-Kernel Fisher analysis method (G-EKFM) for face recognition, which applies Enhanced Kernel Fisher Model (EKFM) on Gaborfaces derived from Gabor wavelet representation of face images. We show that the EKFM outperforms the Generalized Kernel Fisher Analysis (GKFD) model. The performance of G-EKFM is evaluated on a subset of FERET database and CAS-PEAL database by comparing with various face recognition schemes, such as Eigenface, GKFA, Image-based EKFM, Gabor-based GKFA, and so on.