优化了面部识别性能的分类器学习,提高了安全和监视应用的性能
Jitka Poměnková1, Tobiáš Malach2
1Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082/12, 61600 Brno, Czech Republic.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究优化了用于面部识别模板创建的量子间隔方法 (QIM),提高了准确率4-10%. QIM被证明优于其他方法,提高了安全系统的性能.
科学领域:
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
- 人工智能的人工智能
背景情况:
- 面部识别对于现代安全至关重要.
- 现有的模板创建方法在准确性和可扩展性方面存在局限性.
- 量子间隔方法 (QIM) 显示了改善面部识别的前景.
研究的目的:
- 优化量子间隔方法 (QIM) 以提高面部识别准确度.
- 与其他模板创建技术相比,提供QIM的全面评估.
- 分析QIM的参数设置,以便在安全系统中实际实施.
主要方法:
- 研究了七种模板创建方法,包括基于集群描述和基于估计的方法.
- 扩展了使用显著更大和多样化的面部识别数据库的测试.
- 对QIM的参数设置进行了深入分析,以获得最佳性能.
主要成果:
- 与当代模板创建方法相比,QIM表现出优越的性能.
- 通过自动化QIM参数优化,识别精度提高了4-10%.
- 在不同的数据集中观察到性能增长,对于非常通用的数据集,需要手动调整参数.
结论:
- 优化的QIM显著提高了面部识别模板的创建和整体准确性.
- QIM为推进安全可靠的面部识别系统提供了可行的解决方案.
- 提供了QIM参数设置的建议,以促进其实际应用.
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