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Dual-kernel based 2D linear discriminant analysis for face recognition

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Abstract

This paper proposes a new image feature extraction method for face recognition, called dual-kernel based two dimensional linear discriminant analysis (D-K2DLDA), by integrating multiple kernel discriminant analysis with the existing K2DFDA method. The proposed method deals with a face image directly as a matrix, instead of a stacked vector from rows or columns of the image. Moreover, we separately perform an iterative scheme for kernel parameter optimization for each of the two kernels, based on the maximum margin criterion and the damped Newton’s method, followed by a fusion procedure of the two kernels. Experimental results on the ORL and UMIST face databases show the effectiveness of D-K2DLDA.

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Acknowledgments

This work is partially supported by the Project for College High Level Talents of Guangdong Province (2013-221).

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Authors and Affiliations

  1. School of Computer Science, Dongguan University of Technology, Dongguan, 523808, China

    Xiao-Zhang Liu

  2. School of Electronics and Information Engineering, Heyuan Polytechnic, Heyuan, 517000, China

    Hong-Wei Ye

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  1. Xiao-Zhang Liu
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  2. Hong-Wei Ye
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Correspondence to Xiao-Zhang Liu.

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Liu, XZ., Ye, HW. Dual-kernel based 2D linear discriminant analysis for face recognition. J Ambient Intell Human Comput 6, 557–562 (2015). https://doi.org/10.1007/s12652-014-0230-2

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  • DOI: https://doi.org/10.1007/s12652-014-0230-2

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