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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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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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用轻量级和保护隐私的联合面部情绪识别来进行心理健康监测的可解释框架.

Dina Shehada1,2, Hissam Tawfik1,3, Ahmed Bouridane4

  • 1Department of Electrical Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
概括

这项研究引入了一个保护隐私的面部情绪识别 (FER) 框架,使用联合学习和可解释性. 该系统增强了对心理健康监测应用程序的信任和透明度.

关键词:
辅助技术是指辅助技术的使用.可解释的人工智能 (XAI)面部情绪识别 (FER) 功能联合学习的联合学习模型的解释性可解释性值得信赖的AI 值得信赖的AI

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科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 心理学 心理学 心理学

背景情况:

  • 面部情绪识别 (FER) 系统对于心理健康监测至关重要,但由于集中数据,面临隐私和信任问题.
  • 当前FER系统缺乏透明度,阻碍其在心理健康评估等敏感应用中被采用.

研究的目的:

  • 提出一个联合,可解释性驱动的FER框架,确保隐私和可信度.
  • 开发一种轻量级的卷积神经网络 (CNN),用于实时,准确的情感识别.
  • 评估框架的性能及其解释的可靠性.

主要方法:

  • 为FER开发一个轻量级的卷积神经网络 (CNN).
  • 实施联合学习方法,以保护数据隐私.
  • 使用Grad-CAM++进行模型解释,并使用基于扰动的指标 (IAUC,DAUC,AD,IC,ADA) 评估解释.

主要成果:

  • 拟议的模型在多个数据集 (RAF-DB,ExpW,FER2013) 中实现了75.5% (集中) 和74.3% (联合) 的平均准确率.
  • 与现有方法相比,证明了交叉数据集概括的改进.
  • 定量分析证实,模型可解释性提高了透明度,并与性能改善相关.

结论:

  • 联合,可解释性驱动的FER框架为心理健康监测提供了可信且保护隐私的解决方案.
  • 轻量级的CNN模型提供了高精度和强大的概括的实时推断.
  • 模型可解释性是建立信任和提高FER系统在心理健康应用中的可靠性的关键.