Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28928
Title: A novel nonlinear feature extraction and recognition approach based on improved 2D Fisherface plus Kernel discriminant analysis
Authors: Yao, YF
Li, S
Shao, ZL
Jing, XY
Zhang, D 
Yang, JY
Issue Date: 2008
Source: Proceedings - 2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008, 2008, v. 3, 4740013, p. 333-337 How to cite?
Abstract: A novel nonlinear feature extraction and recognition approach which is based on improved 2D Fisherface plus Kernel discriminant analysis is proposed. We provide an improved 2D Fisherface method that designs a new strategy to select appropriate 2D principal components and discriminant vectors, then we use 2D features to perform the Kernel discriminant analysis. The nearest neighbor classifier with cosine distance measure is adopted in classifying the nonlinear discriminant features. The experiments show that the proposed approach achieves better recognition results than several representative discriminant methods.
Description: 2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008, Shanghai, 21-22 December 2008
URI: http://hdl.handle.net/10397/28928
ISBN: 9780769534978
DOI: 10.1109/IITA.2008.223
Appears in Collections:Conference Paper

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