一个新的DeepMaskNet模型用于面具检测和面具面部识别
Naeem Ullah1, Ali Javed2, Mustansar Ali Ghazanfar3
1Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan.
概括
这项研究介绍了DeepMasknet,这是一种用于检测面罩使用和识别戴口罩的人的新框架. 还开发了一个新的数据集,MDMFR,以评估这些关键的公共卫生技术.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 公共卫生 公共卫生
背景情况:
- 由于COVID-19大流行,公众需要戴口罩,这给手动执法和传统面部识别系统带来了挑战.
- 面罩阻碍了标准面部识别算法的有效性,这些算法是为没有面罩的面部设计的.
- 需要自动化系统来检测口罩合规性,并对戴口罩的人进行面部识别.
研究的目的:
- 开发一个强大的框架,DeepMasknet,用于同时检测面罩和面具面部识别.
- 创建一个大规模,多样化的数据集 (MDMFR) 来评估面罩检测和面罩面部识别方法.
- 与现有模型相比,证明拟议的DeepMasknet框架的优越性.
主要方法:
- 提出了一个新的DeepMasknet框架,集成面罩检测和面具面部识别功能.
- 开发了一个全面而多样化的口罩检测和蒙面面部识别 (MDMFR) 数据集.
- 在多个数据集上进行了广泛的实验,包括交叉数据集评估.
主要成果:
- DeepMasknet框架在面具检测和面具面部识别任务中表现出卓越的性能.
- 实验结果验证了在各种数据集中提出的框架的有效性.
- 开发的MDMFR数据集对于基准绩效的比较非常有价值.
结论:
- 深度口罩网框架为公共卫生监测提供了一个强大的解决方案,解决了面具授权的背景下传统系统的局限性.
- 该MDMFR数据集为未来面具面部分析研究提供了标准化的基准.
- 开发的系统对需要可靠的识别和合规监控的现实应用具有显著的前景.
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