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一种新的,快速的,定量方法,用于面部歧视.

Kerri Walter1, Peter Bex1

  • 1Northeastern University, Boston, MA, United States of America.

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PubMed
概括
此摘要是机器生成的。

我们开发了一种新的自适应方法,以远程测量面部歧视能力. 这种方法表明,当多个特征同时变化时,与单个特征变化相比,人们在区分面孔方面更好.

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科学领域:

  • 心理学 心理学 心理学
  • 计算机视觉 计算机视觉
  • 人类的感知 人类的感知

背景情况:

  • 面部歧视是关键的认知能力.
  • 现有的评估面部歧视的方法不是自我管理或适应性的.
  • 需要一种定量,可适应的方法来进行精确的测量.

研究的目的:

  • 开发和验证一种自我管理的,适应性方法来量化面部歧视能力.
  • 为了比较面部歧视门的实验室和远程评估.
  • 调查单个特征变化与组合特征变化对歧视的影响.

主要方法:

  • 使用食交互式D-prime (FInD) 与巴塞尔面模型进行定量评估.
  • 对面部结构主要组件 (PCs) 变化的测量灵敏度.
  • 远程和实验室门测量的比较.
  • 对单个和组合的特征变化进行评估的歧视,有和没有面部旋转.

主要成果:

  • 观察者对面部结构的前10个主要组成部分最敏感.
  • 面部歧视的远程和实验室门并没有显著不同.
  • 对于综合特征变化而言,歧视门明显低于单一变化.
  • 面部旋转增加了歧视值,但对单个和组合变化的影响并不显著.

结论:

  • 食交互式D-prime (FInD) 方法为远程评估面部歧视能力提供了有效和可靠的工具.
  • 结合的面部特征变化比个体变化更容易被歧视.
  • 开发的方法为面部感知和相关领域的未来研究提供了一个有希望的途径.