基于直方图的局部描述符的参数优化,用于面部表情识别
Antoine Badi Mame1, Jules-Raymond Tapamo1
1Discipline of Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Durban, KwaZulu-Natal, South Africa.
PeerJ. Computer science
|July 6, 2023
概括
面部识别可以提高面部表情识别 (FER) 率. 优化局部描述符,如面向梯度 (HOG) 和局部二进制模式 (LBP) 的直方图,进一步提高系统性能,以进行强大的面部分析.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 模式识别 模式识别
背景情况:
- 面部表情识别 (FER) 需要强大的特征描述器.
- 描述者必须处理尺度,照明,姿势和噪音的变化.
研究的目的:
- 评估FER的空间修改局部描述符的有效性.
- 研究面部识别对识别准确性的影响.
- 优化四个关键局部描述符的参数:HOG,LBP,CLBP和WLD.
主要方法:
- 从注册面孔和未注册面孔中提取特征的比较分析.
- 对定向梯度 (HOG),局部二进制模式 (LBP),复合局部二进制模式 (CLBP) 和韦伯局部描述器 (WLD) 的参数进行系统优化.
主要成果:
- 面部识别显著提高了FER系统的识别率.
- 优化的参数选择提高了局部描述符的性能.
- 与最先进的方法相比,提出的方法显示出具有竞争力的结果.
结论:
- 面部识别是准确FER的关键预处理步骤.
- 谨慎的参数调整对于最大限度地发挥本地特征描述符的潜力至关重要.
- 空间修改的局部描述符,当优化时,为面部表情识别提供强大的特征提取.
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