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A New Weighted Sparse Representation Based on MSLBP and Its Application to Face Recognition

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Partially Supervised Learning (PSL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8183))

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Abstract

Face recognition via sparse representation-based classification has received more and more attention in recent years. This approach has achieved state-of-the-art results, which outperforms traditional methods, especially when face image pixels are corrupted or occluded. In this paper, we propose a new weighted sparse representation method called WSRC-MSLBP which utilizes the multi-scale LBP (MSLBP) feature to measure similarity between face images, and to form the weight matrix. The proposed WSRC-MSLBP method not only represents the test sample as a sparse linear combination of all the training samples, but also makes use of locality of local binary pattern. Experimental results on publicly available databases show that the proposed WSRC-MSLBP method is more effective than sparse representation-based classification algorithm and the original weighted sparse representation method.

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Acknowledgments

This work was supported by the 111 Project of Chinese Ministry of Education, Grant No. B12018.

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Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi, 214122, China

    He-Feng Yin & Xiao-Jun Wu

Authors
  1. He-Feng Yin
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  2. Xiao-Jun Wu
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Corresponding author

Correspondence to Xiao-Jun Wu .

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Editors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, People's Republic of China

    Zhi-Hua Zhou

  2. Abt. Neuroinformatik, Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

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Yin, HF., Wu, XJ. (2013). A New Weighted Sparse Representation Based on MSLBP and Its Application to Face Recognition. In: Zhou, ZH., Schwenker, F. (eds) Partially Supervised Learning. PSL 2013. Lecture Notes in Computer Science(), vol 8183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40705-5_10

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  • DOI: https://doi.org/10.1007/978-3-642-40705-5_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40704-8

  • Online ISBN: 978-3-642-40705-5

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