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改进的局部描述器 (ILD):面部识别中的新型融合方法
1Department of CSE, Graphic Era Deemed to be University, Dehradun, Uttarakhand India.
International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management
|May 31, 2023
概括
一个新的改进局部描述器 (ILD) 融合了四个特征描述器 (LBP,ELBP,MBP,LPQ) 以提高图像识别. 这种融合方法在基准数据集上显著优于个别描述符和现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 模式识别 模式识别
- 机器学习 机器学习
背景情况:
- 功能描述符对于图像识别至关重要.
- 与单个描述符相比,合并多个特征可以提高识别率.
- 现有的方法通常依赖于单个描述符,限制性能.
研究的目的:
- 介绍一个新的融合局部描述符,即改进局部描述符 (ILD).
- 结合局部二进制模式 (LBP),扩展LBP (ELBP),中位基线概况 (MBP) 和局部相量化 (LPQ) 的优势.
- 根据个别描述符和基准方法评估ILD的性能.
主要方法:
- 通过将LBP,ELBP,MBP和LPQ的特征集成到组图特征中来开发ILD.
- 使用主要组件分析 (PCA) 来进行特征压缩.
- 使用支持矢量机器 (SVM) 和神经网络 (NN) 来进行分类.
主要成果:
- 与个人描述符 (LBP,ELBP,MBP,LPQ) 相比,ILD表现优越.
- 融合描述器在ORL,GT和Faces94面部识别数据集上取得了最先进的结果.
- ILD有效地将其组成描述符的优点结合起来,实现了强大的特征提取.
结论:
- 提议的改进局部描述器 (ILD) 在图像识别准确度方面提供了显著的改进.
- 功能融合是开发更强大的局部描述符的可行策略.
- ILD为各种模式识别和计算机视觉应用提供了一个有前途的替代方案.
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