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

Na Lin1, Yan Ding2, Yulei Tan2

  • 1Library, Jilin Agricultural University, Changchun, China.

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Summary

This study enhances library access control using an improved face recognition method. The new system achieves high accuracy and speed, boosting library security and management efficiency.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Security

Background:

  • Traditional face recognition methods struggle with varying illumination and posture in library access control.
  • Existing systems face limitations in security and management efficiency due to environmental challenges.

Purpose of the Study:

  • To develop an improved face recognition algorithm for Library Access Control Systems (LACS).
  • To enhance the accuracy and efficiency of face recognition under complex environmental conditions.

Main Methods:

  • An improved Principal Component Analysis (PCA) algorithm was developed, incorporating a beta prior and full probability Bayesian model.
  • The Aggregating Spatial Embeddings for Face Recognition (ASEF) algorithm was combined with the optimized PCA.
  • K-means Clustering Algorithm (KA) was integrated to further refine recognition performance.

Main Results:

  • The improved PCA method demonstrated an average recognition rate of 92.6%.
  • The system achieved a high accuracy of 96%, outperforming related methods.
  • An average recognition speed of 0.40s was recorded, indicating efficient processing.

Conclusions:

  • The proposed face recognition method significantly improves library access control security and management efficiency.
  • The integration of ASEF, optimized PCA, and KA offers a robust solution for real-world face recognition challenges.
  • The system provides rapid and accurate personnel identification, enhancing overall library operations.