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Eigenfeature Regularization and Extraction in Face Recognition
March 2008 (vol. 30 no. 3)
pp. 383-394
ASCII Text
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Xudong Jiang, Bappaditya Mandal, Alex Kot, "Eigenfeature Regularization and Extraction in Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 383-394, March, 2008.
 
 
BibTex
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@article{ 10.1109/TPAMI.2007.70708,
author = {Xudong Jiang and Bappaditya Mandal and Alex Kot},
title = {Eigenfeature Regularization and Extraction in Face Recognition},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {3},
issn = {0162-8828},
year = {2008},
pages = {383-394},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70708},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Eigenfeature Regularization and Extraction in Face Recognition
IS - 3
SN - 0162-8828
SP383
EP394
EPD - 383-394
A1 - Xudong Jiang,
A1 - Bappaditya Mandal,
A1 - Alex Kot,
PY - 2008
KW - Face recognition
KW - linear discriminant analysis
KW - regularization
KW - feature extraction
KW - subspace methods
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
This work proposes a subspace approach that regularizes and extracts eigenfeatures from the face image. Eigenspace of the within-class scatter matrix is decomposed into three subspaces: a reliable subspace spanned mainly by the facial variation, an unstable subspace due to noise and finite number of training samples and a null subspace. Eigenfeatures are regularized differently in these three subspaces based on an eigenspectrum model to alleviate problems of instability, over-fitting or poor generalization. This also enables the discriminant evaluation performed in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR and GT databases show that our method consistently outperforms others.

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Index Terms:
Face recognition, linear discriminant analysis, regularization, feature extraction, subspace methods
Citation:
Xudong Jiang, Bappaditya Mandal, Alex Kot, "Eigenfeature Regularization and Extraction in Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 383-394, March 2008, doi:10.1109/TPAMI.2007.70708
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