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Singular value decomposition based virtual representation for face recognition

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Abstract

Sparse representation, which uses a test sample to represent a linear combination of an entire set of training samples, has achieved great success in face recognition, and it results in good performance when sufficient training samples exist. However, the available number of images of a subject’s face is usually limited in real face recognition systems. In this paper, to obtain more facial representations, we propose a novel method that applies singular value decomposition (SVD) to produce virtual images from original images. The obtained virtual images not only enlarge the size of the set of training samples but also represent relatively stable low frequency facial information; thereby improving the robustness and classification accuracy. We also integrate these virtual samples with the original samples, providing more available information for object classification and, consequently, achieving better performance. To the best of our knowledge, this paper is the first work to use the product of a singular value matrix and right singular vectors to generate virtual samples for face recognition. Experiments on the most widely used and challenging benchmark datasets demonstrate that our method obtains better accuracy and is more robust compared with previous methods.

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Acknowledgments

The work reported in this paper is supported in part by the Science and Technology Foundation of Guizhou province under Grant LH20147597, in part by the Natural Science Foundation of Shenzhen under Grant JCYJ20160506172651253 and Grant JCYJ20160307154003475, and in part by the National Natural Science Foundation of China under Grant 61401287.

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Authors and Affiliations

  1. College of Computer Science and Technology, Guizhou University, Guiyang, China

    Guiying Zhang

  2. Department of Medical Information Engineering, Zunyi Medical University, Zunyi, China

    Guiying Zhang & Xianjie Zhang

  3. Shenzhen Key Laboratory of Advanced Telecommunication and Information Processing, College of Information Engineering, Shenzhen University, Shenzhen, China

    Wenbin Zou

  4. The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen, China

    Yong Zhao

Authors
  1. Guiying Zhang
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  2. Wenbin Zou
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  3. Xianjie Zhang
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  4. Yong Zhao
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Correspondence to Yong Zhao.

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Zhang, G., Zou, W., Zhang, X. et al. Singular value decomposition based virtual representation for face recognition. Multimed Tools Appl 77, 7171–7186 (2018). https://doi.org/10.1007/s11042-017-4627-8

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  • DOI: https://doi.org/10.1007/s11042-017-4627-8

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