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Multi-level Fisher vector aggregated completed local fractional order derivative feature vector for face recognition

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Abstract

In this paper, we propose an image feature extraction method, multi-level Fisher vector aggregated completed local fractional order derivative feature vector (mFVFD), for face recognition. The novelties of our method are summarized as follows: (1) We propose multi-value local fractional order derivative feature vector to analyze the image local intrinsic edge information. (2) We define local multi-structure model to describe the variety and complexity of image structure feature. (3) We developed multiple kernel learning based multi-level Fisher vectors with different number of Gaussian components feature fusion method to capture different levels of the image characteristics. Extensive experiments are conducted on four standard face databases and the results have demonstrated that our proposed method outperforms the state-of-the-art.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61906153 and Natural Science Foundation of Shaanxi Province (China) under Grant No. 2020JQ-651.

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Authors and Affiliations

  1. Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing, Xi’an, China

    Jing Li, Yajun Chen & Erhu Zhang

  2. Information Science Department, Xi’an University of Technology, Xi’an, China

    Jing Li, Yajun Chen & Erhu Zhang

Authors
  1. Jing Li
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  2. Yajun Chen
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  3. Erhu Zhang
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Correspondence to Jing Li.

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Communicated by F. Wu.

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Li, J., Chen, Y. & Zhang, E. Multi-level Fisher vector aggregated completed local fractional order derivative feature vector for face recognition. Multimedia Systems 28, 2357–2365 (2022). https://doi.org/10.1007/s00530-022-00964-0

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