Eigenspectra Versus Eigenfaces: Classification with a Kernel-Based Nonlinear Representor

This short paper proposes a face recognition scheme, wherein features called eigenspectra are extracted successively by the fast Fourier transform (FFT) and the principle component analysis (PCA) and classification results are obtained by a classifier called kernel-based nonlinear representor (KNR)....

Full description

Saved in:
Bibliographic Details
Published inAdvances in Natural Computation pp. 660 - 663
Main Authors Liu, Benyong, Zhang, Jing
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
Face Image
Face Recognition
Fast Fourier Transform
Linear Discriminant Analysis
Pattern recognition. Digital image processing. Computational geometry
Recognition Rate
Face recognition
Image processing
Fast Fourier transformation
Classification
Facies
Database
Pattern recognition
Pattern extraction
Principal component analysis
Kernel method
Online AccessGet full text

Cover

More Information
Summary:This short paper proposes a face recognition scheme, wherein features called eigenspectra are extracted successively by the fast Fourier transform (FFT) and the principle component analysis (PCA) and classification results are obtained by a classifier called kernel-based nonlinear representor (KNR). Its effectiveness is shown by experimental results on the Olivetti Research Laboratory (ORL) face database.
Bibliography:Supported by the Key Project of Chinese Ministry of Education (No.105150). Thanks to Prof. H. Ogawa of Tokyo Institute of Technology for helpful discussions.
ISBN:3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539087_83