两个头比一个好:双重系统在面部比较中获得更好的性能
Zhihui Li1, Lanchi Xie2, Huaqing Song1
1Institute of Forensic Science, Ministry of Public Security, China.
Forensic science international
|November 10, 2023
概括
本研究介绍了一种用于法医面部图像比较的双系统模型,其性能优于单个系统. 开发的贝叶斯网络方法提高了准确性,可以扩展到多个系统.
科学领域:
- 法医科学 法医科学 法医科学
- 计算机视觉 计算机视觉
- 生物识别信息 生物识别信息
背景情况:
- 面部图像比较在法医科学中至关重要.
- 现有的研究主要集中在单一系统的比较上.
- 多个面部识别系统的整合仍未得到充分探索.
研究的目的:
- 开发和评估用于法医面部比较的双系统模型.
- 调查通过结合SeetaFace和FaceNet提供的性能提升.
- 为评估多系统面部比较模型建立一个强大的框架.
主要方法:
- 使用SeetaFace和FaceNet构建一个双系统模型.
- 应用贝叶斯网络作为基础框架.
- 在CelebA数据集上进行大规模实验.
- 基于分数的概率比率的评估,使用实证交叉,成本概率比率和极限蒂佩特图.
- 用瓦瑟斯坦距离评估详细的概率比率表现.
主要成果:
- 与单个系统相比,双系统模型显示出更高的概率比率性能.
- 提出的模型构建和评估方法证明有效.
- 这种方法可以适应整合三个或更多的面部识别系统.
结论:
- 结合多个面部识别系统,如SeetaFace和FaceNet,显著提高了法医面部比较的准确性.
- 贝叶斯网络框架为多系统集成提供了可扩展和有效的方法.
- 该研究的方法论为推进法医生物识别提供了有价值的工具.
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