一个自动改进的面部表情识别,用于面具面孔
Yasmeen ELsayed1, Ashraf ELSayed1,2, Mohamed A Abdou3
1Faculty of Science, Alexandria University, Alexandria, Egypt.
Neural computing & applications
|June 5, 2023
概括
这项研究介绍了一种混合深度学习模型,用于掩盖面部情绪识别. 这种新的方法显著提高了检测情绪的准确性,尽管面罩面临挑战.
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:
- 自动面部表情识别 (AFER) 对于社交互动至关重要.
- 随着COVID-19大流行和强制性口罩的使用,AFER系统面临重大挑战.
- 现有的机器学习技术难以识别面具面孔的情绪.
研究的目的:
- 开发一个准确的AFER方法,用于面具面孔.
- 改善面具条件下的基本情绪 (愤怒,幸福,悲伤,惊喜,蔑视,厌恶,恐惧) 的识别.
主要方法:
- 开发了一种混合卷积神经网络 (CNN) 模型.
- 局部二进制模式 (LBP) 被集成用于准确的特征提取,特别是面部蒙面区域.
- 该模型在CK,CK+和M-LFW-FER数据集上进行了评估.
主要成果:
- 提出的方法在识别面具面孔上的三个情绪方面达到70.76%的准确率.
- 性能与现有的AFER技术相比进行了评估.
- 与以前的方法相比,在准确度方面取得了显著的改进.
结论:
- 混合CNN-LBP模型为面罩的AFER提供了一个强大的解决方案.
- 这一进步对于在后COVID-19时代保持社交互动和人机互动至关重要.
- 进一步的研究可以探索更广泛的情感集和多样化的数据集.
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