基于纹理的特征提取,使用符号模式进行面部表情识别
Mukku Nisanth Kartheek1,2, Munaga V N K Prasad1, Raju Bhukya2
1Institute for Development and Research in Banking Technology, Hyderabad, India.
Cognitive neurodynamics
|May 3, 2024
概括
新的符号模式 (SP) 通过提取基于纹理的特征来改善面部表情识别 (FER). 与现有技术相比,这些新的方法在多个数据集中显示出更高的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 面部表情对于人类的社会互动和情感传递至关重要.
- 有效的特征提取对于高性能自动面部表情识别 (FER) 系统至关重要.
研究的目的:
- 提出新的基于纹理的特征描述器,用于在FER系统中提取面部特征.
- 评估这些新描述符的性能与现有方法相比.
主要方法:
- 三个符号模式 (SP1,SP2,SP3) 的介绍,灵感来自面部特征提取的 Swastik 符号.
- SP1使用3x3社区,而SP2和SP3使用5x5社区,具有不同的像素比较.
- 在多个基准数据集 (MUG, TFEID, CK+, KDEF, FER2013, FERG) 上评估具有多种权重方案 (自然,斐波那契,奇数,素数,平方,二进制) 的SP方法.
主要成果:
- 拟议的符号模式证明了在FER任务中识别准确度的提高.
- 实验分析证实了SP方法在各种数据集中的有效性.
- 与现有的FER技术进行比较,发现识别性能明显提升.
结论:
- 新的"符号模式"为提高FER系统中面部特征提取提供了一个有希望的方法.
- 提出的方法为自动情绪识别领域做出了宝贵的贡献.
- 进一步的研究可以探索这些基于纹理的描述符的变化和应用.
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