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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.

Aesthetic plastic surgery

|July 17, 2023

概括

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.

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.

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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