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http://dx.doi.org/10.7840/KICS.2011.36C.7.428

Elongated Radial Basis Function for Nonlinear Representation of Face Data  

Kim, Sang-Ki (연세대학교 전기전자공학과 영상인식 연구실)
Yu, Sun-Jin (LG전자 전자기술원/미래IT 융합 연구소)
Lee, Sang-Youn (연세대학교 전기전자공학과 영상인식 연구실)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.36, no.7C, 2011 , pp. 428-434 More about this Journal
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.
Keywords
face recognition; subspace learning; kernel feature extraction; RBF kernel function; nearest feature line;
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