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Robust regression based face recognition with fast outlier removal

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Abstract

In this paper, we propose a new robust face recognition method through pixel selection. The method is based on the subspace assumption that a face can be represented by a linear combination in terms of the samples from the same subject. In order to obtain a reliable representation, only a subset of pixels with respect to smallest residuals are taken into the estimation. Outlying pixels which deviate from the linear model of the majority are removed using a robust estimation technique — least trimmed squares regression (LTS). By this method, the representation residual with each class is computed from only the clean data, which gives a more discriminant classification rule. The proposed algorithm provides a novel way to tackle the crucial occlusion problem in face recognition. Evaluation of the proposed algorithm is conducted on several public databases for the cases of both artificial and nature occlusions. The promising results show its efficacy.

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Acknowledgments

Wankou Yang was supported by NSFC under project No.61375001.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Fumin Shen

  2. School of Automation, Southeast University, Nanjing, 210096, China

    Wankou Yang

  3. School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China

    Hanxi Li

  4. School of Computing, National University of Singapore, Singapore, Singapore

    Hanwang Zhang

  5. School of Information Technology & Electrical Engineering, The University of Queensland, Brisbane, Australia

    Heng Tao Shen

Authors
  1. Fumin Shen
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  2. Wankou Yang
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  3. Hanxi Li
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  4. Hanwang Zhang
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  5. Heng Tao Shen
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Correspondence to Hanxi Li.

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Shen, F., Yang, W., Li, H. et al. Robust regression based face recognition with fast outlier removal. Multimed Tools Appl 75, 12535–12546 (2016). https://doi.org/10.1007/s11042-014-2340-4

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  • DOI: https://doi.org/10.1007/s11042-014-2340-4

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