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Research on dimensionality reduction in unconstrained face image based on weighted block tensor sparse graph embedding

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Abstract

How to effectively reduce the dimensionality of high-dimensional and massive unconstrained face data is important. In this paper, the Weighted Tensor Sparse Graph Embedding (WBTSGE) algorithm is proposed. The original sample image is divided into B blocks, and each image block is represented by a second-order tensor. Then, the algorithm introduces the sample category label and added the intra-class compactness constraint. The sparse reconstruction coefficient is constrained by adding distance weight. Finally, the optimal transformation matrix is obtained by adding global constraint factors. The experimental results show that the recognition rate of WBTSGE is 98.57% in the AR face database and 99.80% in the Extended Yale B database. The WBTSGE algorithm improves the accuracy of face recognition in the experimental face database collected in the controlled environment and the real face database collected in the uncontrolled environment.

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Availability of data and materials

The datasets analyzed during the current study are available, including AR database, extended Yale B database, LFW database and PubFig database.

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Funding

This work was supported by National Natural Science Foundation of China (61903183) and Universities Basic Science (Natural Science) Research Project of Jiangsu Province (22KJB460024).

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Authors and Affiliations

  1. College of Information and Communication Engineering, Nanjing Institute of Technology, Hongjing Avenue 1, Nanjing, 211167, Jiangsu, China

    Yangyang Liu, Ying Tong, Xuehong Cao & Lin Meng

  2. Electronic and Communication Engineering, Nanjing University of Posts and Telecommunications, New Model Road 66, Nanjing, 210003, Jiangsu, China

    Zhiqiang Wang

Authors
  1. Yangyang Liu
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  2. Ying Tong
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  3. Zhiqiang Wang
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  4. Xuehong Cao
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  5. Lin Meng
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Contributions

YL contributed to validation, writing—reviewing and editing. YT contributed to conceptualization and methodology. ZW contributed to software, writing—original draft preparation. XC contributed to supervision. LM contributed to visualization and investigation.

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Correspondence to Yangyang Liu.

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Liu, Y., Tong, Y., Wang, Z. et al. Research on dimensionality reduction in unconstrained face image based on weighted block tensor sparse graph embedding. SIViP 17, 1873–1881 (2023). https://doi.org/10.1007/s11760-022-02398-7

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  • DOI: https://doi.org/10.1007/s11760-022-02398-7

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