Feature extraction using kernel Laplacian maximum margin criterion

Zhongxi Sun; Changyin Sun; Wankou Yang; Zhenyu Wang

doi:10.1117/1.OE.51.6.067012

11 June 2012 Feature extraction using kernel Laplacian maximum margin criterion

Zhongxi Sun, Changyin Sun, Wankou Yang, Zhenyu Wang

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Optical Engineering, Vol. 51, Issue 6, 067012 (June 2012). https://doi.org/10.1117/1.OE.51.6.067012

Abstract

We present a novel scheme of feature extraction, namely kernel Laplacian maximum margin criterion, for face recognition. The proposed method seeks to maximize the difference, rather than the ratio, of the determinant between the between-class Laplacian scatter matrix and within-class Laplacian scatter matrix in the implicit feature space via kernel trick. The proposed method not only can produce nonlinear discriminant features, but also does not need to calculate the inverse within-class Laplacian scatter matrix. Experimental results on ORL, FERET, and AR databases validate the effectiveness of the proposed method.

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Zhongxi Sun, Changyin Sun, Wankou Yang, and Zhenyu Wang "Feature extraction using kernel Laplacian maximum margin criterion," Optical Engineering 51(6), 067012 (11 June 2012). https://doi.org/10.1117/1.OE.51.6.067012

Published: 11 June 2012

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KEYWORDS

Feature extraction

Databases

Principal component analysis

Autoregressive models

Optical engineering

Facial recognition systems

Pattern recognition

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