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Construction of a smart face recognition model for university libraries based on FaceNet-MMAR algorithm
Plos One
|January 11, 2024
Summary
This study enhances facial recognition for smart university libraries. The optimized model achieves high accuracy and user satisfaction, improving campus digitization.
Area of Science:
- Computer Science
- Artificial Intelligence
- Information Technology
Background:
- Digital transformation is advancing campus infrastructure.
- Facial recognition technology offers potential for smart library development.
Purpose of the Study:
- To optimize facial recognition algorithms for higher education smart libraries.
- To develop an intelligent management system centered on facial recognition.
Main Methods:
- Designed a smart university library management system using facial recognition.
- Optimized the FaceNet model by integrating MobileNet, Attention mechanism, Receptive field module, and Mish activation function.
- Developed an improved multitask face recognition convolutional neural network.
Main Results:
- Achieved a feature matching error of 0.04 in a stable state.
- Reached a recognition accuracy of 99.05% on the dataset.
- Reported a recognition error rate as low as 0.51%.
Conclusions:
- The developed facial recognition model demonstrates strong performance for smart library applications.
- The system achieved high user satisfaction, with 97.6% for teachers and 96.8% for students.
- Provides effective technical support for building intelligent university libraries.
