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Weighted Huber constrained sparse face recognition

  • Dajiang LeiEmail author
  • Zhijie Jiang
  • Yu Wu
Original Article
  • 18 Downloads

Abstract

Recently sparse coding based on regression analysis has been widely used in face recognition research. Most existing regression methods add an extra constraint factor to the coding residual to make the fidelity term in the \(l_{2}\) loss approach the Gaussian or Laplace distribution. But the essence of these methods is that only the fidelity term of \(l_{1}\) loss or \(l_{2}\) loss is used. In this paper, weighted Huber constrained sparse coding (WHCSC) is used to study the robustness of face recognition in occluded environments, and alternating direction method of multipliers is used to solve the problem of model minimization. In WHCSC, we propose a sparse coding with weight learning and use Huber loss to determine whether the fidelity is a \(l_{2}\) loss or \(l_{1}\) loss. For the WHCSC model, the two kinds of classification modes and the two kinds of weight coefficients are further studied for the intra-class difference and the inter-class difference in the face image classification. Through a large number of experiments on a public face database, WHCSC shows strong robustness in face occlusion, corrosion and illumination changes comparing to the state-of-the-art methods.

Keywords

Sparse coding Face recognition Robustness ADMM 

Notes

Acknowledgement

This paper is supported by the following foundations or programs, including Chongqing Innovative Project of Overseas Study (No. cx2018120), National Social Science Foundation of China (No. 17XFX013). The authors would like to thank the anonymous referees for their valuable comments and suggestions.

Compliance with ethical standards

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Weighted Huber Constrained Sparse Face Recognition”.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of ComputerChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.Institute of Web IntelligenceChongqing University of Posts and TelecommunicationsChongqingChina

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