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Robust Face Recognition Based on Supervised Sparse Representation

  • Jian-Xun Mi
  • Yueru Sun
  • Jia Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

Sparse representation-based classification (SRC) has become a popular methodology in face recognition in recent years. One widely used manner is to enforce minimum \( l_{ 1} \)-norm on coding coefficient vector, which requires high computational cost. On the other hand, supervised sparse representation-based method (SSR) realizes sparse representation classification with higher efficiency by representing a probe using multiple phases. Nevertheless, since previous SSR methods only deal with Gaussian noise, they cannot satisfy empirical robust face recognition application. In this paper, we propose a robust supervised sparse representation (RSSR) model, which uses a two-phase scheme of robust representation to compute a sparse coding vector. To solve the model of RSSR efficiently, an algorithm based on iterative reweighting is proposed. We compare the RSSR with other state-of-the-art methods and the experimental results demonstrate that RSSR obtains competitive performance.

Keywords

Face recognition Huber loss Supervised sparse representation 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.College of Computer and Information SciencesChongqing Normal UniversityChongqingChina

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