精细对齐,灵活融合:面部识别中的多模型分数融合的新框架
Lanchi Xie1, Zhihui Li2, Shengjin Wang3
1Institute of Forensic Science, Ministry of Public Security, China; Department of Electronic Engineering, Tsinghua University, China.
Forensic science international
|December 4, 2023
概括
这项研究引入了法医面部识别的新框架,改善了多模型得分融合. 精细对齐和灵活的融合框架 (FAFF) 通过对齐来自不同面部模型的相似度得分来提高准确性.
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:
- 多模型分数融合是法医面部识别的关键挑战,因为分数分布的差异很大.
- 现有的分数处理方法难以准确对准各种面部模型输出.
研究的目的:
- 提出一个新的分数融合框架,即精细对齐和灵活融合框架 (FAFF),以解决法医面部识别的瓶.
- 开发使用基于分数的概率比率对不同面部模型的相似度得分进行对齐的方法.
主要方法:
- 开发了精细调整和灵活融合框架 (FAFF),使用基于分数的概率比率进行分数调整.
- 建立了一个统一的校准测试工作流程,基于法医概率比率测试.
- 提出了三种基于概率比率 (LLR) 的基方法 (LLRBA1,LLRBA2,LLRBA3) 和一种基于LLR曲线的方法 (LLRBC).
- 使用四个面部模型进行了融合实验:VGGface,Facenet,Arcface和SFace.
主要成果:
- 在CelebA数据集中,与MOEBA和PAN方法相比,LLRBC方法在10-7的假阳性率 (FPR) 中显著提高了175.4%和162.9%的真阳性率 (TPR).
- 与现有方法相比,LLRBA方法显示了显著的改进,TPR@10-7 FPR分别增加了55.6%和48.5%.
- 从多个面部识别模型中,FAFF在对齐和融合分数方面表现出卓越的表现.
结论:
- 拟议的精细对齐和灵活融合框架 (FAFF) 有效地克服了法医面部识别中现有的分数融合技术的局限性.
- 开发的基于LLR的方法,特别是LLRBC,为多模型人脸识别系统提供了在准确性和可靠性方面的显著性能提升.
- FAFF提供了一种强大而灵活的方法,用于整合多样化的面部识别模型,增强法医识别能力.
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