一个集成的面部识别系统用于课堂资源优化,使用MobileNet和SSA-SVM
1Department of Information Engineering, Jiaozuo Normal College, Jiaozuo, 454000, China. fanky_72@jzsz.edu.cn.
Scientific reports
|November 29, 2025
概括
本研究介绍了一种深度学习面部识别系统,用于高效的大学课堂管理. 该系统通过实时的出席跟踪和监控来提高资源利用率,提高准确性和处理速度.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 计算机视觉 计算机视觉
背景情况:
- 对学术机构而言,有效利用教育资源至关重要.
- 实时监控和出席跟踪是优化课堂管理的关键.
- 现有的系统可能缺乏动态教育环境所需的准确性和速度.
研究的目的:
- 开发和评估一个集成的面部识别系统,以加强大学课堂管理.
- 通过实时监控和出席跟踪来改善资源分配和利用.
- 在识别准确性和实时处理能力方面评估系统的性能.
主要方法:
- 使用MobileNet进行特征提取,并与SSA-SVM分类模型相结合.
- 实现一个深度可分离的卷积网络与一个倒置的残余模块,用于精确的特征提取.
- 测试河南省各机构的系统,以评估绩效.
主要成果:
- 与基线方法相比,面部检测准确度增加了3.47%,识别率增加了7.05%.
- 在面部识别方面达到98.13%的整体准确度.
- 在课堂环境中以每秒125的速度显示了93.61%的准确性,在效率方面超过了现有的方法.
结论:
- 集成的面部识别系统有效地支持高效的课堂管理和资源优化.
- 拟议的深度学习模型显示了教育应用的准确性和实时处理的显著改进.
- 这项技术为提高学术环境中的教育资源利用提供了可行的解决方案.
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