使用深度学习在图像中检测情绪的情境检测
Fatiha Limami1, Boutaina Hdioud1, Rachid Oulad Haj Thami1
1Advanced Digital Enterprise Modeling and Information Retrieval (ADMIR) Research Laboratory, Information Retrieval and Data Analytics Team (IRDA), ENSIAS, Mohammed V University, Rabat, Morocco.
Frontiers in artificial intelligence
|July 2, 2024
概括
这项研究通过整合上下文和肢体语言分析来增强人工智能 (AI) 的情感检测能力. 开发的深度学习模型显著提高了识别图像中的情绪的准确性.
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:
- 计算机化情绪检测对于理解人类情绪至关重要.
- 深度神经网络的进步使人工智能能够考虑环境,社会和文化因素,以及面部表情.
研究的目的:
- 为医学和社交媒体分析领域的应用开发更富同情心的AI系统.
- 通过结合上下文和非语言线索来提高图像中情感识别的准确性.
主要方法:
- 使用真实图像数据集 (EMOTIC, EMODB_SMALL, FRAMESDB) 进行模型培训.
- 开发了包括DCNN和VGG19在内的深度学习算法,优化了超参数以实现上下文理解.
- 将分立的情绪类别与连续的情绪维度合并为全面的情绪识别.
主要成果:
- 在情绪识别方面取得了显著的性能改善.
- 情绪_识别_模型和VGG19_contexte模型的平均精度 (mAP) 分别提高了42.81%和44.12%.
- 在捕捉各种情绪在各种环境中的表现优于以前的方法.
结论:
- 这项研究在图像中的上下文情绪识别方面取得了重大进展.
- 潜在的应用包括社会机器人,人机交互和人机通信.
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