Palmprint Recognition Based on Unsupervised Subspace Analysis

As feature extraction techniques, Kernel Principal Component Analysis (KPCA) and Independent Component Analysis (ICA) can both be considered as generalization of Principal Component Analysis (PCA), which has been used for palmprint recognition and gained satisfactory results [3], therefore it is nat...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 675 - 678
Main Authors Feng, Guiyu, Hu, Dewen, Li, Ming, Zhou, Zongtan
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Vector space
Independent component analysis
Classification
Feature extraction
Pattern recognition
Pattern extraction
Principal component analysis
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Summary:As feature extraction techniques, Kernel Principal Component Analysis (KPCA) and Independent Component Analysis (ICA) can both be considered as generalization of Principal Component Analysis (PCA), which has been used for palmprint recognition and gained satisfactory results [3], therefore it is natural to wonder the performances of KPCA and ICA on this issue. In this paper, palmprint recognition using the KPCA and ICA methods is developed and compared with the PCA method. Based on the experimental results, some useful conclusions are drawn, which fits into the scene for a better picture about considering these unsupervised subspace classifiers for palmprint recognition.
ISBN:3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539087_86