在不同的面部特征下使用近集合理论设计面部相似技术
Roshni S Khedgaonkar1, Kavita R Singh1
1Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India.
概括
近集理论为人脸识别提供了一种新的方法,有效地处理表情和整形手术的变化. 这种方法通过使用近集合理论来进行特征选择和识别来实现高精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 模式识别 模式识别
背景情况:
- 近集合理论为基于描述性特征的对象相似性测量提供了一个框架.
- 人类对物体相似性的感知是近集合理论的关键灵感.
- 现有的面部识别方法与面部表情和整形手术引起的变化作斗争.
研究的目的:
- 通过近集合理论提出一种新的面部识别方法.
- 为了应对面部表情和整形手术导致的面部识别方面的挑战.
- 为了利用近集合理论来进行特征选择和识别.
主要方法:
- 利用近集合理论作为具有容忍类的特征选择器来识别关键的面部特征.
- 采用近集合理论作为识别器,使用深度学习模型提取的突出的内在面部特征.
- 在各种面部数据集上进行实验,包括YALE,PSD和ASPS.
主要成果:
- 在YALE面部数据集上实现了93%的准确性.
- 在PSD数据集上证明了98%的准确性.
- 在ASPS数据集上获得了98%的准确性.
- 在面部相似性分类方面表现优于几种最先进的分类方法.
结论:
- 提出的基于近集合理论的面部识别方法有效地处理面部特征的变化.
- 近集理论被证明是面部识别中特征选择和分类的强大工具.
- 该方法在与现有的最先进的算法相比,显示出更高的性能.
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