增强了AlexNet与Gabor和本地二进制模式特征,以改善面部情绪识别
Furkat Safarov1, Alpamis Kutlimuratov2, Ugiloy Khojamuratova3
1Department of Computer Engineering, Gachon University, Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了使用深度学习来改善人机交互的增强面部情绪识别 (FER) 模型. 该模型在基准数据集上实现了高精度,即使有硬件限制.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 面部情绪识别 (FER) 对人机交互和人工智能系统至关重要.
- 现实世界的应用程序经常面临硬件限制,需要高效的FER模型.
- 整合认知和情绪智能可以增强机器与人类的互动.
研究的目的:
- 为面部情绪识别 (FER) 提出一个增强的深度学习模型.
- 为了应对现实世界FER应用中低硬件规格的挑战.
- 提高FER系统的准确性和适应性.
主要方法:
- 利用深度学习的进步来开发FER模型.
- 使用Gabor和局部二进制模式 (LBP) 来提取纹理特征.
- 将功能集成到已修改的AlexNet架构中.
主要成果:
- 在FER2013数据集上达到98.10%的准确性,在RAF-DB数据集上达到93.34%的准确性.
- 在两个数据集上都表现出高精度,回忆和F1分数.
- 在各种操作条件下展示了模型的稳定性和性能.
结论:
- 拟议的FER模型提供了高精度的情绪识别.
- 该模型适合在资源有限的环境中部署.
- 这项研究通过先进的人工智能,有助于更有效的人机交互.
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