对基于机器的情感识别现有能力和未来方向的审查
Rafał Gasz1, Zineb Bougriche2, Jakub Osuchowski2
1Department of Artificial Intelligence, Faculty of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland. r.gasz@po.edu.pl.
Cognitive, affective & behavioral neuroscience
|February 3, 2026
概括
机器情绪识别对于许多应用程序至关重要,但面临着挑战. 本综述涵盖了各种方法,从面部暗示到生理信号,以推进自动化的情感计算.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 情感计算是一种情感计算.
背景情况:
- 机器情感识别对于医疗保健,营销和安全等各个领域的智能系统至关重要.
- 精确的情绪检测是复杂的,因为情绪细微差别,文化差异和技术限制.
- 对人类情绪状态的自动理解提高了用户参与度和系统响应能力.
研究的目的:
- 提供当前基于机器的情感识别技术的全面审查.
- 突出各种方法,包括非生理学和生理学基于信号的方法.
- 建立一个基础,为未来的研究在自动化情感理解.
主要方法:
- 面部表情分析 (FER) 面部表情分析
- 眼镜测量 (OM) 是指眼睛的测量.
- 微表达式识别 (MER) 是指微表达式的识别.
- 语音情感识别 (SER) 是一种语言识别技术.
- 分析身体姿势,手势,步态和触觉互动.
- 基于文本的情绪识别和自我报告方法.
- 生理信号分析:呼吸信号 (RS),电磁皮肤反应 (GSR),脑电图 (EEG),肌电图 (EMG),皮肤温度 (SKT),心脏信号 (ECG,PPG,HRV) 和触摸动力学 (TD).
主要成果:
- 这篇论文回顾了一系列用于机器情绪识别的当代方法.
- 它根据输入方式对技术进行分类,包括视觉,听觉,文字和生理信号.
- 详细检查了EEG,GSR和心脏信号等生理信号.
结论:
- 这篇评论巩固了当前关于机器情绪识别技术的知识.
- 它确定了阻碍该领域进步的关键方法和挑战.
- 这份全面的概述是推动自动化情感计算研究的基础.
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