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2009 10th Workshop on Image Analysis for Multimedia Interactive Services
Why the alternative PCA provides better performance for face recognition
London, United Kingdom
May 06-May 08
ISBN: 978-1-4244-3609-5
ASCII Text
x 
 
I Gede Pasek Suta Wijaya, Keiichi Uchimura, Zhencheng Hu, "Why the alternative PCA provides better performance for face recognition," Image Analysis for Multimedia Interactive Services, International Workshop on, pp. 149-152, 2009 10th Workshop on Image Analysis for Multimedia Interactive Services, 2009.
 
 
BibTex
x
 
@article{ 10.1109/WIAMIS.2009.5031454,
author = {I Gede Pasek Suta Wijaya and Keiichi Uchimura and Zhencheng Hu},
title = {Why the alternative PCA provides better performance for face recognition},
journal ={Image Analysis for Multimedia Interactive Services, International Workshop on},
volume = {0},
year = {2009},
isbn = {978-1-4244-3609-5},
pages = {149-152},
doi = {http://doi.ieeecomputersociety.org/10.1109/WIAMIS.2009.5031454},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Image Analysis for Multimedia Interactive Services, International Workshop on
TI - Why the alternative PCA provides better performance for face recognition
SN - 978-1-4244-3609-5
SP149
EP152
A1 - I Gede Pasek Suta Wijaya,
A1 - Keiichi Uchimura,
A1 - Zhencheng Hu,
PY - 2009
VL - 0
JA - Image Analysis for Multimedia Interactive Services, International Workshop on
ER -
 
I Gede Pasek Suta Wijaya, Computer Science and Electrical Engineering of GSST, Kumamoto University, Japan
Keiichi Uchimura, Computer Science and Electrical Engineering of GSST, Kumamoto University, Japan
Zhencheng Hu, Computer Science and Electrical Engineering of GSST, Kumamoto University, Japan
This paper presents an alternative to PCA technique, called as APCA, which uses within class scatter rather than global covariance matrix. The APCA technique produces better features cluster than does common PCA (CPCA) because it keep the null spaces which contain good discriminant information. The proposed technique achieves better performance for both recognition rate and accuracy parameters than those of CPCA when it was tested using several databases (ITS-LAB., INDIA, ORL, and FERET).
Citation:
I Gede Pasek Suta Wijaya, Keiichi Uchimura, Zhencheng Hu, "Why the alternative PCA provides better performance for face recognition," wiamis, pp.149-152, 2009 10th Workshop on Image Analysis for Multimedia Interactive Services, 2009
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