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A Unified Framework for Subspace Face Recognition
September 2004 (vol. 26 no. 9)
pp. 1222-1228
ASCII Text
x 
 
Xiaogang Wang, Xiaoou Tang, "A Unified Framework for Subspace Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1222-1228, September, 2004.
 
 
BibTex
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@article{ 10.1109/TPAMI.2004.57,
author = {Xiaogang Wang and Xiaoou Tang},
title = {A Unified Framework for Subspace Face Recognition},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {26},
number = {9},
issn = {0162-8828},
year = {2004},
pages = {1222-1228},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.57},
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 - A Unified Framework for Subspace Face Recognition
IS - 9
SN - 0162-8828
SP1222
EP1228
EPD - 1222-1228
A1 - Xiaogang Wang,
A1 - Xiaoou Tang,
PY - 2004
KW - Face recognition
KW - subspace analysis
KW - PCA
KW - LDA
KW - Bayesian analysis
KW - eigenface.
VL - 26
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
PCA, LDA, and Bayesian analysis are the three most representative subspace face recognition approaches. In this paper, we show that they can be unified under the same framework. We first model face difference with three components: intrinsic difference, transformation difference, and noise. A unified framework is then constructed by using this face difference model and a detailed subspace analysis on the three components. We explain the inherent relationship among different subspace methods and their unique contributions to the extraction of discriminating information from the face difference. Based on the framework, a unified subspace analysis method is developed using PCA, Bayes, and LDA as three steps. A 3D parameter space is constructed using the three subspace dimensions as axes. Searching through this parameter space, we achieve better recognition performance than standard subspace methods.

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Index Terms:
Face recognition, subspace analysis, PCA, LDA, Bayesian analysis, eigenface.
Citation:
Xiaogang Wang, Xiaoou Tang, "A Unified Framework for Subspace Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1222-1228, Sept. 2004, doi:10.1109/TPAMI.2004.57
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