通过确定概率比率来评估自动面部识别的匹配信心:一个案例研究
Claudio Ciampini1, Giuliano Iacobellis2, Federico Zomparelli3
1Scientific Investigations Department of Parma, Carabinieri Scientific Investigation Group, Parma, Italy.
Journal of forensic sciences
|November 21, 2025
概括
本研究引入了一种新的法医面部检查 (FFE) 工作流程,将自动面部识别 (AFR) 与贝叶斯统计数据用于概率比 (LR) 计算. 这种方法为面部比较提供了法律上可接受的统计证据,提高了法医可靠性.
科学领域:
- 法医科学 法医科学 法医科学
- 计算机科学 计算机科学
- 统计分析 统计分析
背景情况:
- 法医面部检查 (FFE) 传统上依赖于专家的手动比较.
- 自动面部识别 (AFR) 使用人工智能识别嫌疑人,但需要专家验证.
- 目前的方法缺乏强大的统计框架,用于法院的可接受性.
研究的目的:
- 开发一个创新的FFE工作流程,将AFR输出与贝叶斯统计分析结合起来.
- 为面部比较提供法庭可接受的统计结果.
- 提高面部识别的可靠性和法律责任.
主要方法:
- 使用自动化软件生成面部图像匹配分数.
- 通过核密度估计 (KDE) 和贝叶斯统计模型计算概率比率 (LRs).
- 使用Tippett Plots的验证与欧洲法医科学研究所网络 (ENFSI) 的指导方针保持一致.
主要成果:
- 拟议的框架产生了面部比较的法庭可接受的统计结果.
- 该方法通过严格的统计验证来确保法医可靠性.
- 在增强的工作流程中保持实践人员监督.
结论:
- 创新的FFE工作流程有效地将AFR与贝叶斯统计数据集成在一起,以实现可靠的面部识别.
- 这种方法支持法医从业者提供统计学上可靠的证据,用于法庭演示.
- 该方法已与ENFSI专家分享,表明其可能被广泛采用.
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