自主面部分类在线自我训练系统使用预训练的ResNet50和多项素朴贝叶斯.
Łukasz Maciura1, Tomasz Cieplak1,2, Damian Pliszczuk1
1Research and Development Center, Netrix S.A., 20-704 Lublin, Poland.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一个自主实时面部识别系统,使用预训练的ResNet50模型和多项素朴贝叶斯. 该系统有效地学习新面孔,新奇的检测对于准确的人类分类至关重要.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现有的面部识别卷积神经网络需要大量的训练数据和时间,这取决于硬件.
- 经过预先训练的模型,如ResNet50,在删除分类器层后,可以高效地编码面部图像.
- 面部识别的实时自主学习在数据采集和模型适应方面提出了挑战.
研究的目的:
- 开发一种用于实时人脸识别的新型自主学习系统.
- 为了实现持续的学习和个人分类,而无需人工干预.
- 调查新奇性检测在自主人脸识别系统中的作用.
主要方法:
- 使用预训练的ResNet50模型来编码摄像机捕获的面部图像.
- 雇员多名义天真贝叶斯分类器用于自主,实时的人类分类.
- 集成基于支持矢量机 (SVM) 的新奇性检测算法,以识别未知的面孔进行训练.
主要成果:
- 该系统在有利的条件下证明了成功的自主学习和正确识别新面孔.
- 新鲜事物检测算法对系统识别和学习新身份的能力至关重要.
- 新闻检测中的假阳性可能导致错误分类,赋予多个身份或错误分组.
结论:
- 拟议的自主系统有效地执行实时面部识别和学习.
- 新闻检测机制是确保系统准确性和可靠性的关键组成部分.
- 进一步完善新品检测对于防止动态环境中的错误分类错误至关重要.
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