Two-fold regularization for kernel Fisher discriminant analysis in face recognition

Sang Ki Kim; Kar Ann Toh; Sangyoun Lee

doi:10.1587/elex.6.540

Two-fold regularization for kernel Fisher discriminant analysis in face recognition

Sang Ki Kim, Kar Ann Toh, Sangyoun Lee

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Due to the inherent nature of kernel implementation, the kernel Fisher discriminant suffers from the small sample size problem. In this paper, we introduce a novel variant of the kernel Fisher discriminant formulation to circumvent this problem. By adopting a two-fold regularization scheme on the scatter matrices, we show both effectiveness and reliability of the proposed method particularly regarding the small sample size and the lack of dimensionality issues.

Original language	English
Pages (from-to)	540-545
Number of pages	6
Journal	ieice electronics express
Volume	6
Issue number	9
DOIs	https://doi.org/10.1587/elex.6.540
Publication status	Published - 2009 May 10

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Electrical and Electronic Engineering

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10.1587/elex.6.540

Cite this

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Two-fold regularization for kernel Fisher discriminant analysis in face recognition

Abstract

All Science Journal Classification (ASJC) codes

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