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相关概念视频

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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Communication is a lifelong learning process. Through therapeutic communication, nurses can collect relevant assessment data, provide education and counseling, and interact during nursing interventions. Sending and receiving messages occur through verbal and nonverbal communication techniques and can happen separately or simultaneously.
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相关实验视频

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定义FaceReader面部表情软件输出标准值的定义

Agnes Zhu1, Thanapoom Boonipat2, Sai Cherukuri2

  • 1Mayo Clinic Alix School of Medicine, 200 1st ST. SW, Rochester, MN, 55905, USA. Zhu.Agnes@mayo.edu.

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PubMed
概括
此摘要是机器生成的。

这项研究通过将其结果与人类对生成的面部图像的解释进行比较,来验证FaceReader软件用于面部情绪分析. FaceReader与人类情绪检测有很强的相关性,使其成为研究的可靠工具.

关键词:
整形外科整形美容手术人工智能的人工智能是人工智能.外科手术的结果

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科学领域:

  • 计算机视觉 计算机视觉
  • 心理学 心理学 心理学
  • 人与计算机的交互

背景情况:

  • FaceReader软件利用计算机视觉进行面部表情识别,在学术研究中越来越受欢迎.
  • 它旨在加快,扩展和降低与面部情绪分析相关的成本.
  • 本研究将FaceReader的输出与人类的解释进行比较,以建立软件标准.

研究的目的:

  • 为了比较FaceReader软件在面部情绪识别方面的准确性与人类判断.
  • 根据人类的解释来定义FaceReader软件输出的标准值.
  • 评估FaceReader在不同年龄组的可靠性.

主要方法:

  • 生成性对抗性网络创建了随机的面部图像用于分析.
  • 496名调查参与者和FaceReader软件分析了这些图像.
  • 记录和比较了面部情绪,强度和年龄.

主要成果:

  • 在FaceReader和人类情绪识别之间发现了强烈的整体相关性 (κ = 0.77).
  • 相关性因年龄而异:在儿童中公平 (κ = 0.40),在年轻人中完美 (κ = 1.0),在中年人中强 (κ = 0.79),在老年人中近乎完美 (κ = 0.9).
  • 分析了80张图像,包括同数量的儿童,年轻人,中年人和老年人.

结论:

  • 这项研究为FaceReader软件在分析微笑面部表情方面提供了第一个基准.
  • 已建立的标准可以在使用FaceReader的未来研究中使用.
  • FaceReader表现出高度的准确性,可与人类情感识别评估器相提并论.