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First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06)
Palmprint Recognition Based on 2-Dimension PCA
Beijing, China
August 30-September 01
ISBN: 0-7695-2616-0
ASCII Text
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Junwei Tao, Wei Jiang, Zan Gao, Shuang Chen, Chao Wang, "Palmprint Recognition Based on 2-Dimension PCA," Innovative Computing ,Information and Control, International Conference on, vol. 1, pp. 326-330, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06), 2006.
 
 
BibTex
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@article{ 10.1109/ICICIC.2006.132,
author = {Junwei Tao and Wei Jiang and Zan Gao and Shuang Chen and Chao Wang},
title = {Palmprint Recognition Based on 2-Dimension PCA},
journal ={Innovative Computing ,Information and Control, International Conference on},
volume = {1},
year = {2006},
isbn = {0-7695-2616-0},
pages = {326-330},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICICIC.2006.132},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Innovative Computing ,Information and Control, International Conference on
TI - Palmprint Recognition Based on 2-Dimension PCA
SN - 0-7695-2616-0
SP326
EP330
A1 - Junwei Tao,
A1 - Wei Jiang,
A1 - Zan Gao,
A1 - Shuang Chen,
A1 - Chao Wang,
PY - 2006
KW - null
VL - 1
JA - Innovative Computing ,Information and Control, International Conference on
ER -
 
Junwei Tao, Shandong University, China
Wei Jiang, Shandong University, China
Zan Gao, Shandong University, China
Shuang Chen, Shandong University, China
Chao Wang, Shandong University, China
PCA (principal component analysis) is a successful feature detection method for pattern recognition. It is the optimal dimension compression technique based on second-order information, in the sense of mean-square error. It deals with image vector whose dimension is usually high. 2DPCA is a novel PCA method for image matrix, and it can calculate the covariance matrix more precise. In this paper we combined the new 2DPCA method and PCA to palmprint recognition, and first we apply 2DPCA to the image matrix and we make an improvement in the selection of principal components. We select the principal component that is better for classification. Then we apply 1DPCA to the projected vectors for dimension reduction. At last we apply the method to PolyU Palmprint Database. The experiment result shows that our method got more recognition rate with lower dimensions.
Citation:
Junwei Tao, Wei Jiang, Zan Gao, Shuang Chen, Chao Wang, "Palmprint Recognition Based on 2-Dimension PCA," icicic, vol. 1, pp.326-330, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06), 2006
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