Abstract
A novel method with general tensor representation for face recognition based on multilinear nonparametric discriminant analysis is proposed. Traditional LDA-based methods suffer some disadvantages such as small sample size problem (SSS), curse of dimensionality, as well as a fundamental limitation resulting from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. In addition, traditional LDA-based methods and their variants don’t consider the class boundary of samples and interior structure of each sample class. To address the problems, a new multilinear nonparametric discriminant analysis is proposed, and new formulations of scatter matrices are given. Experimental results indicate the robustness and accuracy of the proposed method.
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Zhang, X., Zhang, X., Cao, J., Liu, Y. (2010). Multilinear Nonparametric Feature Analysis. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_54
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DOI: https://doi.org/10.1007/978-3-642-12304-7_54
Publisher Name: Springer, Berlin, Heidelberg
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