强大的面部识别使用四次元区间II型模糊逻辑基于色彩图像的特征提取
Sudesh Yadav1, Virendra P Vishwakarma2
1Department of Higher Education, Govt. College, Ateli, Mahendergarh, Haryana, India. yadavsudesh01@gmail.com.
Medical & biological engineering & computing
|February 1, 2024
概括
我们介绍了一种新的面部识别方法,它结合了四次数,间隔类型II模糊逻辑和确定性学习机器 (DLM). 这种强大的技术通过有效处理色彩信息来提高准确性和速度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 传统的面部识别方法往往忽略了关键的颜色信息和颜色图像中的像素相互依赖.
- 现有的技术缺乏高效处理多通道彩色数据,导致潜在的冗余和精度降低.
研究的目的:
- 通过整合四次数,区间类型II模糊逻辑和确定性学习机器 (DLM) 来开发一种强大的,快速的面部识别学习技术.
- 通过更有效地结合颜色信息和像素关联来解决传统方法的局限性.
主要方法:
- 提出了一种新技术,即基于四次元区间II型的确定性学习机器 (QIntTyII-DLM).
- 颜色面部图像使用四次数序来表示,以捕捉红色,绿色和蓝色 (RGB) 频道之间的相互关系.
- 四边形表示是使用间隔类型II模糊逻辑进行模糊化,以减少像素冗余并将通道转换为正交色彩空间.
- 分类是使用非代的,无参数的确定性学习机器 (DLM) 进行的.
主要成果:
- 拟议的QIntTyII-DLM技术在标准面部数据集 (AR,乔治亚理工学院,印度面部 (女性) 和面部94 (男性)) 上表现得更好.
- 与现有技术相比,该方法可以将百分比错误率降低约10-12%.
- 观察到计算速度的显著改善.
结论:
- 与DLM集成的四次数和区间类型II模糊逻辑与DLM提供了一种优越的面部识别方法.
- QIntTyII-DLM方法有效地利用色彩信息并减少像素冗余,从而提高精度和效率.
- 这种技术在强大而快速的面部识别系统中呈现出有前途的进步.
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