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相关实验视频

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Super-resolution Imaging of Neuronal Dense-core Vesicles
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混合特征提取器使用离散波形变换和基于卷积神经网络的手掌静脉识别面向梯度的直方图.

Meirista Wulandari1, Rifai Chai2, Basari Basari1,3

  • 1Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Jawa Barat, Indonesia.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
概括

这项研究介绍了VeinCNN,一种使用混合特征提取的新型手掌静脉识别方法. VeinCNN实现了生物识别安全系统的高精度和可靠性,在公共数据集上表现出卓越的性能.

关键词:
在美国,CNN是CNN.这就是为什么DWT DWT DWT霍格 (HOG) 的意思静脉CNNN 在线观看掌上静脉 掌上静脉

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科学领域:

  • 计算机科学 计算机科学
  • 生物识别信息 生物识别信息
  • 图像处理 图像处理

背景情况:

  • 生物识别对安全和出席至关重要.
  • 由于其本质性,手掌静脉生物识别提供了增强的安全性.
  • 红外线手掌静脉图像带来了诸如不均照明和低对比度等挑战.

研究的目的:

  • 开发一种准确可靠的手掌静脉识别方法.
  • 为了解决手掌静脉图像中低对比度和不均照明的局限性.
  • 通过使用关键生物识别指标来评估拟议方法的性能.

主要方法:

  • 开发了一个卷积神经网络 (CNN) 模型,命名为VeinCNN.
  • 使用离散波形变换 (DWT) 和面向梯度 (HOG) 的直方图 (Histogram) 采用混合特征提取.
  • 该方法在五个公共数据集上进行了测试:CASIA,Vera,Tongji,PolyU和PUT.

主要成果:

  • 静脉CNN方法在准确性,曲线下面面积 (AUC) 和等错率 (EER) 中显示出有希望的结果.
  • 在CASIA数据集上实现了最高的性能,准确率为99.85%,AUC为99.80,EER为0.0083.
  • 混合特征提取方法在克服图像质量问题方面被证明是有效的.

结论:

  • 拟议的VeinCNN识别方案为基于手掌静脉的生物识别验证提供了一个强大的解决方案.
  • 混合DWT和HOG功能提取有效地提高了识别准确性.
  • VeinCNN显示了安全识别系统中现实应用的巨大潜力.