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PCM 2015: Advances in Multimedia Information Processing -- PCM 2015 pp 453–462Cite as

Marginal Fisher Regression Classification for Face Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9314))

Abstract

This paper presents a novel marginal Fisher regression classification (MFRC) method by incorporating the ideas of marginal Fisher analysis (MFA) and linear regression classification (LRC). The MFRC aims at minimizing the within-class compactness over the between-class separability to find an optimal embedding matrix for the LRC so that the LRC on that subspace achieves a high discrimination for classification. Specifically, the within-class compactness is measured with the sum of distances between each sample and its neighbors within the same class with the LRC, and the between-class separability is characterized as the sum of distances between margin points and their neighboring points from different classes with the LRC. Therefore, the MFRC embodies the ideas of the LRC, Fisher analysis and manifold learning. Experiments on the FERET, PIE and AR datasets demonstrate the effectiveness of the MFRC.

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Acknowledgements

This work was supported by the National Basic Research Program of China (973 Program) under Grant 2014CB340400, the National Natural Science Foundation of China under Grant 61271325, Grant 61472273, and the Elite scholar Program of Tianjin University under Grant 2015XRG-0014.

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Authors and Affiliations

  1. School of Electronic Information Engineering, Tianjin University, Tianjin, 300072, China

    Zhong Ji, Yunlong Yu & Yanwei Pang

  2. Department of Computer Science, State University of New York, Binghamton, NY, 13902, USA

    Zhong Ji, Yingming Li & Zhongfei Zhang

Authors
  1. Zhong Ji
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  2. Yunlong Yu
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  3. Yanwei Pang
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  4. Yingming Li
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  5. Zhongfei Zhang
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Corresponding author

Correspondence to Zhong Ji .

Editor information

Editors and Affiliations

  1. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  2. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Jitao Sang

  3. ICU, IVY Lab, KAIST, Daejeon, Korea (Republic of)

    Yong Man Ro

  4. KAIST, Daejeon, Korea (Republic of)

    Junmo Kim

  5. College of Computer Science, Zhejiang University, Hangzhou, China

    Fei Wu

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© 2015 Springer International Publishing Switzerland

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Ji, Z., Yu, Y., Pang, Y., Li, Y., Zhang, Z. (2015). Marginal Fisher Regression Classification for Face Recognition. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_44

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  • DOI: https://doi.org/10.1007/978-3-319-24075-6_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24074-9

  • Online ISBN: 978-3-319-24075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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