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Mathematical Problems in EngineeringVolume 2014 2014, Article ID 898560, 9 pages

Research Article

School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

Institute of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin 150001, China

Received 15 May 2014; Accepted 16 July 2014; Published 6 August 2014

Academic Editor: Mohamed A. Seddeek

Copyright © 2014 Yue Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Kernel Fisher discriminant analysis KFDA method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis MDKFDA is proposed in this paper. The constructed multiple data-dependent kernel MDK is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm.

Author: Yue Liu, Yibing Li, Hong Xie, and Dandan Liu



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