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Published in: Soft Computing 10/2012

01-10-2012 | Original Paper

Feature extraction using 2DIFDA with fuzzy membership

Published in: Soft Computing | Issue 10/2012

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Abstract

In this paper, a new method called fuzzy two-dimensional inverse Fisher discriminant analysis (fuzzy 2DIFDA) directly based on 2D image matrices rather than image vectors is proposed for feature extraction and recognition. In the proposed method, the distribution information of samples is first characterized using fuzzy set theory, and the corresponding fuzzy scatter matrices are then redefined. Image discriminant features which have embedded the fuzzy information are finally extracted by selecting 2D principal components and 2D inverse Fisher discriminant vectors. Experimental results on FERET face database and FKP database demonstrate the effectiveness of the proposed method.

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Metadata
Title
Feature extraction using 2DIFDA with fuzzy membership
Publication date
01-10-2012
Published in
Soft Computing / Issue 10/2012
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0861-1

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