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.