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17th International Conference on Pattern Recognition (ICPR'04) - Volume 1
Review the Strength of Gabor Features for Face Recognition from the Angle of Its Robustness to Mis-Alignment
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
ASCII Text
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Shiguang Shan, Wen Gao, Yizheng Chang, Bo Cao, Pang Yang, "Review the Strength of Gabor Features for Face Recognition from the Angle of Its Robustness to Mis-Alignment," Pattern Recognition, International Conference on, vol. 1, pp. 338-341, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004.
 
 
BibTex
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@article{ 10.1109/ICPR.2004.1334121,
author = {Shiguang Shan and Wen Gao and Yizheng Chang and Bo Cao and Pang Yang},
title = {Review the Strength of Gabor Features for Face Recognition from the Angle of Its Robustness to Mis-Alignment},
journal ={Pattern Recognition, International Conference on},
volume = {1},
year = {2004},
issn = {1051-4651},
pages = {338-341},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334121},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Review the Strength of Gabor Features for Face Recognition from the Angle of Its Robustness to Mis-Alignment
SN - 1051-4651
SP338
EP341
A1 - Shiguang Shan,
A1 - Wen Gao,
A1 - Yizheng Chang,
A1 - Bo Cao,
A1 - Pang Yang,
PY - 2004
KW - null
VL - 1
JA - Pattern Recognition, International Conference on
ER -
 
Shiguang Shan, Chinese Academy of Sciences, China
Wen Gao, Chinese Academy of Sciences, China
Yizheng Chang, Chinese Academy of Sciences, China
Bo Cao, Chinese Academy of Sciences, China
Pang Yang, Chinese Academy of Sciences, China
Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation.
Citation:
Shiguang Shan, Wen Gao, Yizheng Chang, Bo Cao, Pang Yang, "Review the Strength of Gabor Features for Face Recognition from the Angle of Its Robustness to Mis-Alignment," icpr, vol. 1, pp.338-341, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004
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