Letters: A novel face recognition approach based on kernel discriminative common vectors (KDCV) feature extraction and RBF neural network

Authors:
Xiao-Yuan Jing;Yong-Fang Yao;Jing-Yu Yang;David Zhang
Affiliations:
Institute of Automation, Nanjing University of Posts and Telecommunications, Guangdong Road No. 38, Nanjing 210003, PR China;Institute of Automation, Nanjing University of Posts and Telecommunications, Guangdong Road No. 38, Nanjing 210003, PR China;Institute of Computer Science, Nanjing University of Science and Technology, Nanjing, PR China;Deptartment of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Venue:
Neurocomputing
Year:
2008

Citing 8
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Abstract

The discriminative common vectors (DCV) algorithm is a recently addressed discriminant method, which shows better face recognition effects than some commonly used linear discriminant algorithms. The radial basis function (RBF) neural network is widely applied to the function approximation and pattern classification. One of the interesting research topics of RBF network is how to set appropriate hidden-layer units. Based on DCV, we design a new nonlinear feature extraction algorithm that is the kernel DCV (KDCV) algorithm and we employ the DCV generated by KDCV as the hidden-layer units of the RBF network. Then we present a novel face recognition approach that is the KDCV-RBF approach. Testing on a public large face database (AR database), the experimental results demonstrate that KDCV-RBF is an effective face recognition approach, which outperforms several representative recognition methods.