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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
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