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<p>Being able to experience emotions is a defining characteristic of machine intelligence, and the first step in giving robots emotions is to enable them to accurately recognize and understand human emotions. The initial task to achieve this is to quantify abstract human emotions into concrete data. Combining this with deep learning techniques, a variety of machine models for recognizing human emotions can be constructed to achieve efficient human-robot interaction. Along this line of thought, this paper comprehensively combs through the development paths of emotion quantification, emotion modeling, and machine emotion recognition models based on various signals with practical examples. We focus on summarizing the machine emotion recognition models in recent years, classifying them into four broad categories according to the input signals: vision-based, language-based, physiological signal-based emotion recognition models and multimodal emotion recognition models for in-depth discussion, revealing the strengths and weaknesses of these models and potential application scenarios.In particular, this study identifies multimodal emotion recognition models as a key direction for future research, which significantly improve recognition accuracy and robustness by integrating multiple data sources. Finally, the article discusses the challenges and improvement directions for emotion recognition models, providing an important reference for promoting intelligent and emotional human-computer interaction. Figure&#xa0;<InternalRef RefID="Fig1">1</InternalRef>. shows the framework of this paper.</p>

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A review of the emotion recognition model of robots

  • 期刊论文

  • 发表日期：

    2025年1月22日

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  • Mingyi Zhao,
  • Linrui Gong,
  • Abdul Sattar Din

摘要

Being able to experience emotions is a defining characteristic of machine intelligence, and the first step in giving robots emotions is to enable them to accurately recognize and understand human emotions. The initial task to achieve this is to quantify abstract human emotions into concrete data. Combining this with deep learning techniques, a variety of machine models for recognizing human emotions can be constructed to achieve efficient human-robot interaction. Along this line of thought, this paper comprehensively combs through the development paths of emotion quantification, emotion modeling, and machine emotion recognition models based on various signals with practical examples. We focus on summarizing the machine emotion recognition models in recent years, classifying them into four broad categories according to the input signals: vision-based, language-based, physiological signal-based emotion recognition models and multimodal emotion recognition models for in-depth discussion, revealing the strengths and weaknesses of these models and potential application scenarios.In particular, this study identifies multimodal emotion recognition models as a key direction for future research, which significantly improve recognition accuracy and robustness by integrating multiple data sources. Finally, the article discusses the challenges and improvement directions for emotion recognition models, providing an important reference for promoting intelligent and emotional human-computer interaction. Figure 1. shows the framework of this paper.