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Schatten-p Norm Based Linear Regression Discriminant Analysis for Face Recognition

  • Lijiang Chen
  • Wentao Dou
  • Xia Mao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

Locality-regularized linear regression classification (LLRC) shows good performance on face recognition. However, it sorely performs on the original space, which results in degraded classification efficiency. To solve this problem, we propose a dimensionality reduction algorithm named schatten-p norm based linear regression discriminant analysis (SPLRDA) for image feature extraction. First, it defines intra-class and inter-class scatters based on schatten-p norm, which improves the capability to deal with illumination changes. Then the objective function which incorporates discriminant analysis is derived from the minimization of intra-class compactness and the maximization of inter-class separability. Experiments carried on some typical databases validate the effectiveness and robustness of our method.

Keywords

Dimensionality reduction Schatten-p norm Linear regression Feature extraction Face recognition Discriminant analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringBeihang UniversityBeijingChina

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