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CAGNet:一个结合多层次特征聚合和注意力机制的网络,用于在人机交互中智能面部表情识别

Dengpan Zhang¹, Wenwen Ma¹, Zhihao Shen¹

¹School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China.

Sensors (Basel, Switzerland)

|June 27, 2025

概括

本研究介绍了CAGNet,这是一种用于服务机器人中增强面部表情识别 (FER) 的新型深度学习网络. CAGNet提高了特征表示和准确性,促进了人机交互.

科学领域:

人工智能的人工智能
机器人技术机器人技术机器人技术
计算机视觉计算机视觉

背景情况:

面部表情识别 (FER) 对服务机器人的自然人机交互至关重要.
现有的卷积神经网络 (CNN) 模型在准确的面部表情特征表示方面扎.
服务机器人需要高效的FER用于诸如护理和生产辅助等应用.

研究的目的:

开发一个先进的FER网络,解决当前CNN模型的局限性.
为了提高面部表情的特征表示和识别精度.
为服务机器人的智能升级提供技术解决方案.

主要方法:

提出了CAGNet,这是一个结合多层次特征聚合和注意力机制的新型网络.
采用基于深度学习的分层卷积架构,并使用堆叠的卷积层.
集成卷积块注意模块 (CBAM) 和全球平均汇集 (GAP) 用于本地和全球特征优化.
集成批量规范化 (BN) 和放弃,以提高模型稳定性和通用性.

主要成果:

在FER2013数据集上,CAGNet实现了71.52%的准确性.
在CK+数据集上,CAGNet的准确度达到了97.97%.
实验结果验证了拟议CAGNet方法的有效性和优越性.

结论:

CAGNet为面部表情识别提供了卓越的技术解决方案.
该网络显著提高了特征表示和识别精度.
CAGNet为服务机器人的智能进步提供了强有力的支持.

关键词:

卷积块注意力模块的卷积面部表情识别面部表情识别全球平均值的聚合方式多尺度特征聚合多尺度特征聚合

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