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基于小样本的轻量级手指静脉识别研究.

Yi Ding1,2,3, Kai Wang4, Xiaojun Wu5

  • 1School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China. dingyi@zust.edu.cn.

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概括
此摘要是机器生成的。

本研究介绍了一种轻量级的指纹静脉识别算法,使用修改后的VGG-19网络和Gabor过器. 这种新的方法实现了高精度 (98.45%) 和提高了生物识别的稳定性.

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科学领域:

  • 生物识别信息 生物识别信息
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 指纹静脉识别面临诸如低温效应和概括等挑战.
  • 现有的算法可能需要大量的训练数据和计算资源.

研究的目的:

  • 开发一种轻量级和高效的指纹静脉识别算法.
  • 为了提高算法的稳定性和准确性,使用一个小样本大小.

主要方法:

  • 一个加博波器预处理图像以模拟低温条件,改善模型概括性.
  • 修改后的VGG-19网络具有减少的层次和调整的激活功能,创建了一个轻量级的架构.
  • 集成了一个多重注意力机制,以增强特征提取能力.
  • 转移学习用于减少模型培训时间.

主要成果:

  • 拟议的算法显示了高的识别准确性,达到大约98.45%.
  • 与现有的手指静脉识别技术相比,该方法显示出更好的稳定性和速度.
  • 轻量化设计使得该算法适用于资源有限的环境.

结论:

  • 开发的轻量级手指静脉识别算法为生物识别提供了一个有前途的解决方案.
  • 加博尔波器,网络修改和注意力机制的组合有效地解决了常见的识别问题.
  • 该算法在准确性,稳定性和速度方面实现了卓越的性能,并通过实验结果验证.