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基于FaceNet-MMAR算法的大学图书馆智能面部识别模型的构建

Yan Liu¹, Yan Qu¹

¹Library, Yantai Vocational College, Yantai, China.

|January 11, 2024

概括

这项研究提高了智能大学图书馆的面部识别功能. 优化的模型实现了高精度和用户满意度,改善了校园数字化.

科学领域:

计算机科学计算机科学
人工智能的人工智能
信息技术信息技术信息技术

背景情况:

数字化转型正在推动校园基础设施的发展.
面部识别技术为智能图书馆开发提供了潜力.

研究的目的:

为高等教育智能图书馆优化面部识别算法.
开发一个以面部识别为中心的智能管理系统.

主要方法:

使用面部识别设计了一个智能大学图书馆管理系统.
通过整合MobileNet,注意力机制,感受场模块和Mish激活功能来优化FaceNet模型.
开发了一种改进的多任务人脸识别卷积神经网络.

主要成果:

在稳定状态下实现了0.04的特征匹配错误.
在数据集上达到99.05%的识别准确度.
报告的识别错误率低至0.51%.

结论:

开发的面部识别模型显示了智能图书馆应用程序的强大性能.
该系统获得了高用户满意度,97.6%的教师和96.8%的学生.
为建立智能大学图书馆提供有效的技术支持.

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