IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0313415.html
   My bibliography  Save this article

Optimization design and application of library face recognition access control system based on improved PCA

Author

Listed:
  • Na Lin
  • Yan Ding
  • Yulei Tan

Abstract

The application of face recognition technology in Library Access Control System (LACS) has an important impact on improving the security and management efficiency of the library. However, the traditional face recognition methods have some limitations in the face of complex environmental conditions such as illumination and posture change. To solve this problem, an improved method combining the Aggregating Spatial Embeddings for Face Recognition (ASEF) algorithm and Principal Component Analysis (PCA) is proposed. The PCA algorithm is optimized by introducing beta prior and full probability Bayesian model. In addition, the research also integrates K-means Clustering Algorithm (KA) to further improve the accuracy and efficiency of face recognition. The experiment showed that the improved PCA method had an average recognition rate of 92.6%, an average recognition speed of 0.40s, and higher accuracy compared to other related methods, reaching 96%. In practical applications, the system quickly and accurately completes the identification of personnel entry and exit, and improves the efficiency and security of library access management.

Suggested Citation

  • Na Lin & Yan Ding & Yulei Tan, 2025. "Optimization design and application of library face recognition access control system based on improved PCA," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-22, January.
    • Handle: RePEc:plo:pone00:0313415
    DOI: 10.1371/journal.pone.0313415
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0313415
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0313415&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0313415?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xiaojing Liu & Miaochao Chen, 2022. "Labor Market Resource Allocation Optimization Based on Principal Component Analysis," Journal of Mathematics, Hindawi, vol. 2022, pages 1-11, February.
    2. Meiling Cai & Yaqin Shi & Jinping Liu & Jean Paul Niyoyita & Hadi Jahanshahi & Ayman A. Aly, 2023. "DRKPCA-VBGMM: fault monitoring via dynamically-recursive kernel principal component analysis with variational Bayesian Gaussian mixture model," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2625-2653, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0313415. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.