面部识别算法:一个系统的文献综述
1Department of Computer Engineering, College of Computing, Fahad Bin Sultan University, Tabuk 71454, Saudi Arabia.
Journal of imaging
|February 25, 2025
概括
面部识别技术在深度学习方面取得了重大进展,但在隐私,伦理和偏见方面面临挑战. 负责任的发展需要道德准则和进一步的研究,以确保可靠性和用户信任.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物识别信息 生物识别信息
背景情况:
- 面部识别技术 (FRT) 已经显著发展,影响了各种行业.
- 了解它的原则,指标和应用是至关重要的.
研究的目的:
- 审查面部识别技术的新发展和挑战.
- 探索算法技术,性能指标和健康,社会和安全方面的应用.
主要方法:
- 学术出版物,会议和行业新闻的系统文献综述.
- 重点是深度学习技术,特别是卷积神经网络 (CNN).
主要成果:
- 深度学习,特别是CNN,已经大大提高了FRT的准确性和效率.
- 确定的主要挑战包括隐私问题,道德困境和系统偏见.
结论:
- FRT已经发展,但道德和监管的使用是必不可少的.
- 未来的研究应该专注于可靠性,减少偏见和建立用户信心.
关键词:
适应性增强 (ADA) 是一种卷积神经网络 (CNN) 是一种神经网络.数据集数据集数据集决策树 (DT) 是指一个决策树.固有面的自己面.面部识别算法的算法鱼人的面孔 鱼人的面孔梯度增强可以提高梯度.这就是超参数的超参数.k-最近的邻居 (KNN)简单的海湾 (NB)绩效评价 绩效评价 绩效评价 绩效评价 绩效评价随机森林分类器 (RFC) 是一个随机森林分类器.支持矢量机器 (SVM) 的使用.耶鲁面部识别数据集更多相关视频
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