Elongated Radial Basis Function for Nonlinear Representation of Face Data 


Vol. 36,  No. 7, pp. 428-434, Jul.  2011


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  Abstract

Recently, subspace analysis has raised its performance to a higher level through the adoption of kernel-based nonlinearity. Especially, the radial basis function, based on its nonparametric nature, has shown promising results in face recognition. However, due to the endemic small sample size problem of face data, the conventional kernel-based feature extraction methods have difficulty in data representation. In this paper, we introduce a novel variant of the RBF kernel to alleviate this problem. By adopting the concept of the nearest feature line classifier, we show both effectiveness and generalizability of the proposed method, particularly regarding the small sample size issue.

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  Cite this article

[IEEE Style]

S. Kim, S. Yu, S. Lee, "Elongated Radial Basis Function for Nonlinear Representation of Face Data," The Journal of Korean Institute of Communications and Information Sciences, vol. 36, no. 7, pp. 428-434, 2011. DOI: .

[ACM Style]

Sang-Ki Kim, Sunjin Yu, and Sangyoun Lee. 2011. Elongated Radial Basis Function for Nonlinear Representation of Face Data. The Journal of Korean Institute of Communications and Information Sciences, 36, 7, (2011), 428-434. DOI: .

[KICS Style]

Sang-Ki Kim, Sunjin Yu, Sangyoun Lee, "Elongated Radial Basis Function for Nonlinear Representation of Face Data," The Journal of Korean Institute of Communications and Information Sciences, vol. 36, no. 7, pp. 428-434, 7. 2011.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880