Feature extraction based on fuzzy 2DLDA

Wankou Yang; Xiaoyong Yan; Lei Zhang; Changyin Sun

doi:10.1016/j.neucom.2009.12.025

Feature extraction based on fuzzy 2DLDA

Wankou Yang, Xiaoyong Yan, Lei Zhang, Changyin Sun

Research output: Journal article publication › Journal article › Academic research › peer-review

39 Citations (Scopus)

Abstract

In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA (F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K-nearest neighbor (FKNN), and then incorporate the membership degree into the definition of the between-class scatter matrix and the within-class scatter matrix. Finally, we get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. In our definition of the between-class scatter matrix and within-class matrix, the fuzzy information is better used than fuzzy fisherface. Experiments on the Yale, ORL and FERET face databases show that the new method works well.

Original language	English
Pages (from-to)	1556-1561
Number of pages	6
Journal	Neurocomputing
Volume	73
Issue number	10-12
DOIs	https://doi.org/10.1016/j.neucom.2009.12.025
Publication status	Published - 1 Jun 2010

Keywords

2DLDA
Face recognition
Feature extraction
Fisher
Fuzzy
LDA

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2009.12.025

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title = "Feature extraction based on fuzzy 2DLDA",

abstract = "In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA (F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K-nearest neighbor (FKNN), and then incorporate the membership degree into the definition of the between-class scatter matrix and the within-class scatter matrix. Finally, we get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. In our definition of the between-class scatter matrix and within-class matrix, the fuzzy information is better used than fuzzy fisherface. Experiments on the Yale, ORL and FERET face databases show that the new method works well.",

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AU - Yang, Wankou

AU - Yan, Xiaoyong

AU - Zhang, Lei

AU - Sun, Changyin

PY - 2010/6/1

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N2 - In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA (F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K-nearest neighbor (FKNN), and then incorporate the membership degree into the definition of the between-class scatter matrix and the within-class scatter matrix. Finally, we get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. In our definition of the between-class scatter matrix and within-class matrix, the fuzzy information is better used than fuzzy fisherface. Experiments on the Yale, ORL and FERET face databases show that the new method works well.

AB - In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA (F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K-nearest neighbor (FKNN), and then incorporate the membership degree into the definition of the between-class scatter matrix and the within-class scatter matrix. Finally, we get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. In our definition of the between-class scatter matrix and within-class matrix, the fuzzy information is better used than fuzzy fisherface. Experiments on the Yale, ORL and FERET face databases show that the new method works well.

KW - 2DLDA

KW - Face recognition

KW - Feature extraction

KW - Fisher

KW - Fuzzy

KW - LDA

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DO - 10.1016/j.neucom.2009.12.025

M3 - Journal article

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VL - 73

SP - 1556

EP - 1561

JO - Neurocomputing

JF - Neurocomputing

IS - 10-12

ER -

Feature extraction based on fuzzy 2DLDA

Abstract

Keywords

ASJC Scopus subject areas

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