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Robust intelligent PCA‐based face recognition framework using GNP‐fuzzy data mining
Author(s) -
Zhang Deng,
Mabu Shingo,
Hirasawa Kotaro
Publication year - 2013
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.21847
Subject(s) - facial recognition system , eigenface , computer science , artificial intelligence , principal component analysis , pattern recognition (psychology) , robustness (evolution) , linear discriminant analysis , fuzzy logic , classifier (uml) , cluster analysis , local binary patterns , face (sociological concept) , data mining , machine learning , image (mathematics) , social science , biochemistry , chemistry , sociology , gene , histogram
Abstract Traditional principal component analysis (PCA) based face recognition algorithms have a low recognition accuracy due to the influence of noise and illumination changes. This paper proposes a robust, intelligent PCA‐based face recognition framework in the complicated illumination database when using multiple training images per person (MTIP‐CID). There are mainly two improvements in the proposed method. One is that a face‐recognition‐oriented genetic‐based clustering algorithm is introduced to reduce the influence of a large number of classes on the classification accuracy in the MTIP‐CID. The other is that a classifier based on fuzzy class association rules (FCARs) is applied to mine the inherent relationships between eigenfaces and to improve the robustness of PCA‐based face recognition in noisy environments. Experimental results on the extended Yale‐B database demonstrate that the proposed framework performs better and is more robust against noise compared with other traditional face recognition algorithms, i.e. linear discriminant analysis (LDA) and local binary patterns (LBPs). © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.