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Application of feature extraction using nonlinear dynamic system in face recognition

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Abstract

This paper proposes a nonlinear dynamic system by simulating the sensitivity of V2 (second visual cortex) to the graph. The system uses the auxiliary function matrix and the target image to generate chaotic attractors with initial sensitivity, ergodicity and relatively stable similarity to extract image features. Firstly, select the excellent auxiliary function to construct auxiliary function matrix. Secondly, the target image is optimized for grayscale, and the iteration range is automatically adjusted by the Viola-Jones detector and the curvature of the image. Finally, the auxiliary function matrix and the processed target image are iteratively interleaved to generate chaotic attractors for face recognition. In this paper, we selected Yale, ORL, AR, and Jaffe face database for experiments, and the average recognition rates were 98.14\(\%\), 98.40\(\%\), 97.06\(\%\), and 97.74\(\%\), respectively. Because of its fast speed, simple method, and large room for improvement, this method is expected to be applied in many practical fields and has theoretical value for continued research.

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Data availability

The data that support the findings of this study are available from the corresponding author, [Lianglei Sun], upon reasonable request.

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Authors and Affiliations

  1. College of Information, Dalian University, Dalian, China

    Lianglei Sun, Hongchen Lin, Wanbo Yu & Yi Zhang

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  1. Lianglei Sun
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  2. Hongchen Lin
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  3. Wanbo Yu
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  4. Yi Zhang
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All authors contributed equally to this paper.

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Correspondence to Lianglei Sun.

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Sun, L., Lin, H., Yu, W. et al. Application of feature extraction using nonlinear dynamic system in face recognition. Evolving Systems 14, 825–838 (2023). https://doi.org/10.1007/s12530-022-09468-8

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