Abstract
Gabor-based face representation has achieved enormous success in face recognition. However, one drawback of Gabor-based face representation is the huge amount of data that must be stored. Due to the nonlinear structure of the data obtained from Gabor response, classical linear projection methods like principal component analysis fail to learn the distribution of the data. A nonlinear projection method based on a set of self-organizing maps is employed to capture this nonlinearity and to represent face in a new reduced feature space. The Multiple Self-Organized Gabor Features (MSOGF) algorithm is used to represent the input image using all winner indices from each SOM map. A new local matching algorithm based on the similarity between local features is also proposed to classify unlabeled data. Experimental results on FERET database prove that the proposed method is robust to expression variations.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
| Pages | 2909-2912 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - Nov 18 2010 |
| Event | 2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey Duration: Aug 23 2010 → Aug 26 2010 |
Publication series
| Name | Proceedings - International Conference on Pattern Recognition |
|---|---|
| ISSN (Print) | 1051-4651 |
Other
| Other | 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
|---|---|
| Country | Turkey |
| City | Istanbul |
| Period | 8/23/10 → 8/26/10 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
Cite this
Robust face recognition using multiple self-organized Gabor features and local similarity matching. / Aly, Saleh; Shimada, Atsushi; Tsuruta, Naoyuki; Taniguchi, Rin Ichiro.
Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010. 2010. p. 2909-2912 5597061 (Proceedings - International Conference on Pattern Recognition).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Robust face recognition using multiple self-organized Gabor features and local similarity matching
AU - Aly, Saleh
AU - Shimada, Atsushi
AU - Tsuruta, Naoyuki
AU - Taniguchi, Rin Ichiro
PY - 2010/11/18
Y1 - 2010/11/18
N2 - Gabor-based face representation has achieved enormous success in face recognition. However, one drawback of Gabor-based face representation is the huge amount of data that must be stored. Due to the nonlinear structure of the data obtained from Gabor response, classical linear projection methods like principal component analysis fail to learn the distribution of the data. A nonlinear projection method based on a set of self-organizing maps is employed to capture this nonlinearity and to represent face in a new reduced feature space. The Multiple Self-Organized Gabor Features (MSOGF) algorithm is used to represent the input image using all winner indices from each SOM map. A new local matching algorithm based on the similarity between local features is also proposed to classify unlabeled data. Experimental results on FERET database prove that the proposed method is robust to expression variations.
AB - Gabor-based face representation has achieved enormous success in face recognition. However, one drawback of Gabor-based face representation is the huge amount of data that must be stored. Due to the nonlinear structure of the data obtained from Gabor response, classical linear projection methods like principal component analysis fail to learn the distribution of the data. A nonlinear projection method based on a set of self-organizing maps is employed to capture this nonlinearity and to represent face in a new reduced feature space. The Multiple Self-Organized Gabor Features (MSOGF) algorithm is used to represent the input image using all winner indices from each SOM map. A new local matching algorithm based on the similarity between local features is also proposed to classify unlabeled data. Experimental results on FERET database prove that the proposed method is robust to expression variations.
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UR - http://www.scopus.com/inward/citedby.url?scp=78149481376&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.713
DO - 10.1109/ICPR.2010.713
M3 - Conference contribution
AN - SCOPUS:78149481376
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2909
EP - 2912
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
ER -