在面部识别方面的不同类型的专业知识
Alice Towler1,2, James D Dunn3, Sergio Castro Martínez4
1School of Psychology, University of New South Wales, Sydney, 2052, Australia. a.towler@uq.edu.au.
Scientific reports
|July 14, 2023
概括
面部识别技术和人类专家展示了不同的准确度方法. 通过协作或分数融合,利用这种多样性可以最大限度地提高面部识别准确度,用于关键应用.
科学领域:
- 认知心理学 认知心理学
- 法医科学 法医科学 法医科学
- 计算机视觉 计算机视觉
背景情况:
- 面部识别错误对国家安全,刑事司法,公共安全和公民权利构成风险.
- 准确的面部识别对于各种操作设置至关重要.
研究的目的:
- 为了比较顶级人类面部识别器 (超级识别器),训练有素的法医检查员和深度神经网络 (DNN) 的准确性和认知/感知策略.
- 调查面部识别专业知识的多样性及其对作战用途的影响.
主要方法:
- 一个基于实验室的评估和一个国际能力测试,涉及来自14个国家的27个法医部门.
- 超级识别器,法医检查员和DNN之间的准确性,错误模式,响应时间和信心水平的比较.
主要成果:
- 超级识别者,法医检查员和DNN实现了同等的准确性,但表现出不同的认知和感知策略.
- 超级识别者是快速的,容易产生"同一个人"偏见,具有高可信度的错误,而法医检查员是缓慢的,公正的,并避免错误.
- 人类专家和DNN在面部相似性方面存在分歧,表明面部信息的内部表示不同.
结论:
- 存在多种类型的面部识别专业知识,每个都适合不同的操作角色.
- 通过合作或分数融合,利用各个专家之间的多样性,为最大限度地提高面部识别准确性提供了一种强大的方法.
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