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2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP'06)
A Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree
Pasadena, California, USA
December 18-December 20
ISBN: 0-7695-2745-0
ASCII Text
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Qinzhen Xu, Pinzheng Zhang, Wenjiang Pei, Luxi Yang, Zhenya He, "A Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree," Intelligent Information Hiding and Multimedia Signal Processing, International Conference on, pp. 309-312, 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP'06), 2006.
 
 
BibTex
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@article{ 10.1109/IIH-MSP.2006.9,
author = {Qinzhen Xu and Pinzheng Zhang and Wenjiang Pei and Luxi Yang and Zhenya He},
title = {A Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree},
journal ={Intelligent Information Hiding and Multimedia Signal Processing, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2745-0},
pages = {309-312},
doi = {http://doi.ieeecomputersociety.org/10.1109/IIH-MSP.2006.9},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Information Hiding and Multimedia Signal Processing, International Conference on
TI - A Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree
SN - 0-7695-2745-0
SP309
EP312
A1 - Qinzhen Xu,
A1 - Pinzheng Zhang,
A1 - Wenjiang Pei,
A1 - Luxi Yang,
A1 - Zhenya He,
PY - 2006
KW - null
VL - 0
JA - Intelligent Information Hiding and Multimedia Signal Processing, International Conference on
ER -
 
Qinzhen Xu, Southeast University, China
Pinzheng Zhang, Southeast University, China
Wenjiang Pei, Southeast University, China
Luxi Yang, Southeast University, China
Zhenya He, Southeast University, China
A hybrid learning approach named confusioncrossed support vector machine tree (CSVMT) has been proposed in our current work. It is developed to achieve a better performance for complex distribution problems even when the two parameters of SVM are not appropriately selected. In this paper a facial expression recognition approach based on CSVMT is proposed. Pseudo-Zernike moments are applied in the feature extraction phase, and then CSVMT learning model is performed during the facial expression recognition phase. The compared results on Cohn- Kanade facial expression database show that the proposed approach appeared higher recognition accuracy than the other approaches.
Citation:
Qinzhen Xu, Pinzheng Zhang, Wenjiang Pei, Luxi Yang, Zhenya He, "A Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree," iih-msp, pp.309-312, 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP'06), 2006
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