Abstract
In biometric recognition, feature fusion is an important area of research due to the fact that multiple types of features contain richer and complementary information. Discriminative Correlation Analysis (DCA) is a recently proposed feature fusion method, which incorporates the class association into correlation analysis so that the features not only have the maximum intrinsic correlation between feature sets but also have class structure information. However, DCA is a linear technique, that finds a linear transformation of the original space. For highly nonlinearly distributed data, classification with nonlinear techniques works better than the linear ones. In this paper, we propose Kernel-DCA which generalizes DCA in order to handle nonlinear problems. Similar to Kernel-SVM, Kernel-DCA utilizes a kernel method to map feature sets to a high-dimensional space in which features are linearly separable. Experimental results, for the fusion of ear and face feature, using the WVU database with large variations in pose, show that Kernel-DCA achieves better results on nonlinearly distributed data than DCA and other feature fusion methods.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 24th International Conference on Pattern Recognition, ICPR 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3198-3203 |
| Number of pages | 6 |
| Volume | 2018-August |
| ISBN (Electronic) | 9781538637883 |
| DOIs | |
| State | Published - Nov 26 2018 |
| Event | 24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China Duration: Aug 20 2018 → Aug 24 2018 |
Other
| Other | 24th International Conference on Pattern Recognition, ICPR 2018 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 8/20/18 → 8/24/18 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
Kernel Discriminant Correlation Analysis : Feature Level Fusion for Nonlinear Biometric Recognition. / Bai, Yang; Haghighat, Mohammad; Abdel-Mottaleb, Mohamed.
2018 24th International Conference on Pattern Recognition, ICPR 2018. Vol. 2018-August Institute of Electrical and Electronics Engineers Inc., 2018. p. 3198-3203 8546068.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Kernel Discriminant Correlation Analysis
T2 - Feature Level Fusion for Nonlinear Biometric Recognition
AU - Bai, Yang
AU - Haghighat, Mohammad
AU - Abdel-Mottaleb, Mohamed
PY - 2018/11/26
Y1 - 2018/11/26
N2 - In biometric recognition, feature fusion is an important area of research due to the fact that multiple types of features contain richer and complementary information. Discriminative Correlation Analysis (DCA) is a recently proposed feature fusion method, which incorporates the class association into correlation analysis so that the features not only have the maximum intrinsic correlation between feature sets but also have class structure information. However, DCA is a linear technique, that finds a linear transformation of the original space. For highly nonlinearly distributed data, classification with nonlinear techniques works better than the linear ones. In this paper, we propose Kernel-DCA which generalizes DCA in order to handle nonlinear problems. Similar to Kernel-SVM, Kernel-DCA utilizes a kernel method to map feature sets to a high-dimensional space in which features are linearly separable. Experimental results, for the fusion of ear and face feature, using the WVU database with large variations in pose, show that Kernel-DCA achieves better results on nonlinearly distributed data than DCA and other feature fusion methods.
AB - In biometric recognition, feature fusion is an important area of research due to the fact that multiple types of features contain richer and complementary information. Discriminative Correlation Analysis (DCA) is a recently proposed feature fusion method, which incorporates the class association into correlation analysis so that the features not only have the maximum intrinsic correlation between feature sets but also have class structure information. However, DCA is a linear technique, that finds a linear transformation of the original space. For highly nonlinearly distributed data, classification with nonlinear techniques works better than the linear ones. In this paper, we propose Kernel-DCA which generalizes DCA in order to handle nonlinear problems. Similar to Kernel-SVM, Kernel-DCA utilizes a kernel method to map feature sets to a high-dimensional space in which features are linearly separable. Experimental results, for the fusion of ear and face feature, using the WVU database with large variations in pose, show that Kernel-DCA achieves better results on nonlinearly distributed data than DCA and other feature fusion methods.
UR - http://www.scopus.com/inward/record.url?scp=85059782576&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059782576&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2018.8546068
DO - 10.1109/ICPR.2018.8546068
M3 - Conference contribution
AN - SCOPUS:85059782576
VL - 2018-August
SP - 3198
EP - 3203
BT - 2018 24th International Conference on Pattern Recognition, ICPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -