在基于深度学习的实时面部识别系统中,使用场景变化指标降低虚假阳性率
Mehmet Ali Kutlugün1, Yahya Şirin1
1Istanbul Sabahattin Zaim University, Computer Science and Engineering, Istanbul, Turkey.
Multimedia tools and applications
|June 26, 2023
概括
这项研究引入了场景变化指标 (SCI),以改善面部识别的深度度度度学习. 在不变的面部场景中,SCI模型通过降低虚假识别率来提高准确性.
科学领域:
- 计算机视觉和模式识别
- 人工智能和机器学习
背景情况:
- 面部识别系统面临着来自不同照明,面部表情和身体变化的挑战.
- 深度学习,特别是深度度度学习,对于精确的特征提取和分类在人脸识别中至关重要.
- 计算成本和准确性对于实时人脸识别应用至关重要.
研究的目的:
- 开发一种新的场景变化指标 (SCI) 模型,以增强面部识别的深度度度度学习.
- 降低虚假识别率,提高实时人脸识别系统的准确性.
- 优化特征向量的分类值,最大限度地降低计算成本.
主要方法:
- 实施一个深度度度度学习模型,其中包含一个场景变化指标 (SCI).
- 该SCI模型在滑动窗口中识别静态块,以完善比较值.
- 在不变的场景块中提高灵敏度,以减少不必要的样本比较.
主要成果:
- 拟议的SCI模型实现了99.25%的高精度和99.28%的F-1得分.
- 错误识别率显著降低,即使同一个人的面部图像存在差异.
- 通过缩小静态面部场景中的样本比较区域来最大限度地减少错误.
结论:
- 场景变化指标 (SCI) 有效地提高了面部识别深度度度度学习模型的性能.
- 该模型为提高实时面部识别应用程序的准确性和效率提供了一个有前途的解决方案.
- 这种方法成功地减轻了静态场景中人内变异引起的误识挑战.
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